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  1. Transcript: Knowing What You Don’t Know
  2.  
  3. Slide 1: Knowing What You Don't Know
  4. Hello and welcome to PD20 Developing Reasoned Conclusions. My name is Greg Andres, and I will be with you throughout this course. I hope you will have a great work term. And I hope you learn a lot about yourself, about engineering, and what it means to develop professional skills.
  5.  
  6. Slide 2: Professional Expectations
  7. Now apply this to your life right now. You have set out to become a professional engineer. As such, people will expect things of you. For example, consider what Section 77 of the PEO Code of Ethics says: A practitioner shall, 1. Regard the practitioner’s duty to public welfare as paramount; 2. Endeavor at all times to enhance the public regard for the practitioner’s profession by extending the public knowledge thereof and discouraging untrue, unfair or exaggerated statements with respect to professional engineering; 3. Not express publicly, or while the practitioner is serving as a witness before a court, commission or other tribunal, opinions on professional engineering matters that are not founded on adequate knowledge and honest conviction;
  8.  
  9. Slide 3: Professional Expectations
  10. You will be held accountable for what you say. It is expected that you will be truthful. It is expected that you will know what you are talking about. And it is expected that you will have good reasons to back up your claims. This is what will be expected of you as an engineer.
  11.  
  12. Slide 4: Professional Expectations
  13. So how do you get to the position where you are able to speak with authority, and that you can confidently assert that your conclusions are based on good evidence? What can you do now to get to where you need to be? You have taken the first important step by working on an engineering degree from UWaterloo.
  14.  
  15. Slide 5: Engineering Graduate Attributes
  16. Now, to remain accredited, the Faculty of Engineering must comply with the requirements set out by the Canadian Engineering Accreditation Board. The CEAB lists a number of attributes that engineering graduates are expected to have upon completing their degree. Among the attributes are investigation, communication skills, professionalism, ethics and equity and lifelong learning. There are other attributes, but I want to focus on these five for now.
  17.  
  18. Slide 6: Engineering Graduate Attributes
  19. What can you do now to develop these 5 attributes? Practice them. Think of these attributes as practical abilities. And one of the things that you should be doing now, during your work term, is practicing these skills. This requires honest self-assessment, conscious effort, and a lot of work. Mastery doesn’t just happen. Mastery requires effort. And this is why this course is important for you. This course is designed to help you along your journey of becoming a valuable employee, and a consummate professional. This course is here to guide you as you work on your professional skills.
  20.  
  21. Slide 7: Knowing What You Don't Know
  22. How difficult is it for you to admit you don’t know something? As much as we would like to think otherwise, we will never know everything. There will always be something else for us to learn. Acknowledging that we don’t know is important. Think of it this way: if we convince ourselves that we know everything, then there is nothing left for us to learn. However, if we have overestimated our competence and abilities, there will be things for us to learn. Which puts us in a bind: there are things for us to learn, but we are unwilling to learn them because we have convinced ourselves that we already know everything. And when this happens in the workplace, we become intolerable to our co-workers and a problem for our supervisor.
  23.  
  24. Slide 8: Knowing What You Don't Know
  25. So we must learn to acknowledge our ignorance. There is nothing wrong with being ignorant, per se. Ignorance is simply a state of not knowing. If we are in a state of ignorance, we lack the necessary knowledge, or we do not have the relevant information. Again, ignorance isn’t necessarily a bad thing. It’s what you do once you realize that you’re in a state of ignorance that matters. When you identify your state of ignorance and seek to correct it, you are demonstrating intellectual integrity. This brings us to a crucial distinction. It is one thing to be in a state of ignorance. It is an entirely different thing to realize that you are in a state of ignorance.
  26.  
  27. Slide 9: Knowing What You Don't Know
  28. We can make this distinction explicit by using a Johari window. When it comes to knowledge, we either know something or we don’t know it. So going back to Mister Fowler’s story in the Ringwearer video, we either know what structural steel design tables are or we don’t. But not only can we have knowledge about things like catwalks, design tables, and engineering, we can have knowledge about knowledge. If you know something and you know that you know it, then what you have is explicit knowledge.
  29.  
  30. Slide 10: Explicit Knowledge
  31. So what is explicit knowledge? If you have explicit knowledge about a subject, you’ll be able to articulate what you know about that subject. If you have explicit knowledge, you’ll be able to teach and explain what you know to other people. You’ll be able to answer questions and apply what you know to new contexts. And you’ll be able to state what follows as a matter of logical consequence. In short, explicit knowledge should be our goal, and it’s the type of thing that a good university education will provide you with. And the more you learn about engineering and the particular industry you find yourself in, the more valuable you’ll be to have around.
  32.  
  33. Slide 11: Ignorant, but Aware
  34. But not only can we know that we know, we can also know that we don’t know something. In which case we are ignorant, but we are aware of our ignorance. Again, this isn’t necessarily a bad thing; it’s simply a matter of fact.
  35.  
  36. Slide 12: Ignorant, but Aware
  37. So for example, I don’t know how to build a toaster. Put all the parts in front of me and I will just stare at the pile. And I know that I don’t know how to build a toaster. It’s not that I am proud of my ignorance; it’s just that I’ve never had the need to learn. That’s the nice thing about having money in a free market economy. If my toaster breaks, I can pay somebody else to build one for me. Or go back to the catwalk example given by Mister Fowler. Mister Fowler knew that he didn’t know anything about structural steel design tables; of course that’s the sort of thing that you need to know if you’re going to build a catwalk, but since he knew what he didn’t know, he knew what he needed to learn to get the job done. The point is, it’s not that ignorance is a bad thing per say; it’s what you do once you realize that you’re in a state of ignorance that matters.
  38.  
  39. Slide 13: Practical Knowledge
  40. Now this is where it gets interesting. Is it possible to not know that you know something? Now on first blush, this seems like a paradoxical question. But it makes sense if we make the distinction between explicit propositional knowledge (knowledge that you can articulate) and practical knowledge. You can know something but not know that you know it. It’s just that what you have is practical knowledge.
  41.  
  42. Slide 14: Practical Knowledge
  43. What is practical knowledge? Well it’s an ability to do something. Like walking, riding a bike, or whistling. In some cases, you might not be able to articulate what you know. Take my late-father, for example. My father could play pretty much any song on the piano by ear. But if you put sheet music in front of him, he was completely lost. If you hummed the tune for him, he could pick it up and play it immediately. On occasion I asked him, “Dad how do you do that?” To which he always responded, “I don’t know, I just can.” People who can play the piano by ear know how to play the piano, even though they may be at a loss to explain how they do it. They just do it.
  44.  
  45. Slide 15: Practical Knowledge
  46. In some cases, you might not even be aware that you have a particular ability. I think swimming is the most obvious example. Ask me if I know how to swim and I’ll say, I don’t know. I’ve never tried. To find out if I can swim, all you’d have to do is to get me into the pool somehow. And it’s quite likely that I have the ability to stay afloat and propel myself through the water. Swimming is a natural ability that many mammals, including humans, have. Even bats can swim. Swimming is an excellent example of having an ability but not knowing that you have the ability.
  47.  
  48. Slide 16: Ignorant but Unaware
  49. The last quadrant represents a state where we don’t know something and we don’t know that we don’t know. In which case we are ignorant, but unaware that we are ignorant. Now in some sense, this is the state that humankind has found itself in over and over again.
  50.  
  51. Slide 17: Ignorant but Unaware
  52. Before the invention of the telescope we did not know that there are moons orbiting Jupiter. We did not know that we didn’t know, and Galileo’s discovery fundamentally altered our view of the cosmos. People didn’t know any better before Galileo’s discovery, but now that we know that the sun is the centre of our solar system, we would have very little patience for someone who still believed that the planets revolve around the earth.
  53.  
  54. Slide 18: Ignorant but Unaware
  55. Before Einstein, no one knew anything about relativity, and we didn’t know that we didn’t know. But thanks to Einstein’s work, we now know what relativity is (or at the very least, we know that we don’t know what it is). Before the science of genetics took form, we didn’t know that we didn’t know anything about DNA. And again, our new formed understanding has changed the way we approach the diagnosis and treatment of diseases.
  56.  
  57. Slide 19: Blindspot Bias
  58. The point is, ignorance is not, in itself, a bad thing. What matters is how we address our ignorance. And we can easily trip ourselves up if we are not careful. Here’s one way that we can trip ourselves up. Even if we acknowledge that there will be times where we will need more information to reach a reasonable conclusion, we can quickly convince ourselves that we do not, at this moment, need more information.
  59.  
  60. Slide 20: Blindspot Bias
  61. Here is an example. On the next page, you will see an argument. There will be two premises – two claims, and a conclusion. They will be laid out in this form:
  62.  
  63. What I want you to do is assess the argument. Is it a good argument? Is there enough information in the premises to know that the conclusion is true beyond a doubt? Read the two premises and conclusion, then click on the button you think is the correct assessment.
  64.  
  65. Slide 21: Blindspot Bias
  66. Everything about this argument seems so right. Both premises are obviously true, as is the conclusion. Yet the premises do not give us enough information to conclude (beyond a doubt) that some roses fade.
  67.  
  68. Slide 22: Blindspot Bias
  69. What trips us up is our conviction that some roses fade, and this conviction is no doubt informed by our past experiences. So when we are presented with the information in the premises of the argument, we don’t stop to ask ourselves if we need more information to come to the conclusion that some roses fade. The premises and conclusion fit so well with our experiences that we quickly convince ourselves that we do not, in this situation, need more information.
  70.  
  71. Slide 23: Employers
  72. Now this is not to dismiss the value of our experiences. My point is that if we are not careful, what we think we know will affect how we interpret the information and data that we have in front of us. We have an almost limitless capacity to overlook our own ignorance, and this will hinder us in the workplace. What employers are looking for — and what will get you ahead in your professional life — is the ability to recognize that you are in a state of ignorance, a willingness to admit that you don’t know, and a corresponding readiness to do the work to learn what you need to learn to do your job better. In other words, employers want workers with intellectual integrity.
  73.  
  74. © University of Waterloo
  75.  
  76.  
  77.  
  78. Transcript: Finding Information – Part A
  79. Slide 1: Finding Information
  80. [introductory music]
  81.  
  82. Welcome to Unit 2.
  83.  
  84. Unit 1 is all about knowing what you don’t know. This unit is about knowing how to find information.
  85.  
  86. Slide 2: Google Search
  87. Suppose you are working for a pharmaceutical company. You have been tasked to research journals that specialize in clinical cardiology. You don’t have the faintest idea of where to start looking. So you turn to Google and search “journals clinical cardiology”. Of the top six hits, five are journals and one is a Wikipedia article. So you start copying down the names and web addresses of the top five journals. Then your eye catches something at the bottom of the page. Respected medical journal turns to dark sid| Ottawa Citizen.
  88.  
  89. Slide 3: Real Science?
  90. You click on the link and read a story about a once respected Canadian peer-reviewed journal that was sold and is now considered a predatory journal that publishes bunk. Here is what the Ottawa Citizen writes, “Experimental & Clinical Cardiology was published in Oakville, Ontario for 17 years and had a solid reputation for printing original medical research. It was sold in 2013, and its new owners say they are in Switzerland, but do their banking in Turks and Caicos. And for 1,200 dollars U.S., they’ll print anything, even a garbled blend of fake cardiology, Latin grammar and missing graphs submitted by the Citizen.”
  91.  
  92. Slide 4: The Moral
  93. To see what kind of things the journal would publish, the Ottawa citizen plagiarized an article about HIV, but replaced each occurrence of HIV with the word cardiac. They titled the article by joining together nonsensical, but medical sounding jargon. The article was published with no questions asked. The moral: searching for information requires a keen eye, discernment, and a good dose of skepticism.
  94.  
  95. Slide 5: Searching for Information
  96. The importance of doing our due diligence in searching for information cannot be overemphasized. If you buy a cell phone from a phone company, but don’t do any research into the phone or the phone plan, that is on you. If you buy a used car without finding out as much information about the car as you can, that is on you. But as an engineering co-op student, you have an obligation to your company, your peers, and the public. The onus is on you to be as careful and thorough as time allows when searching for information. Sometimes the first hit on Google will suffice for your information needs. Other times, you will need to dig a lot deeper.
  97.  
  98. Slide 6: Searching for Information
  99. Though finding reliable information can feel like a mug’s game, there are strategies to ensure we do our due diligence in our search for information. Here are five questions to ask when searching for information. 1. What are my information needs? 2. How do I start looking? 3. What resources do I have access to? 4. How do I vet sources? 5. How do I properly cite sources?
  100.  
  101. Slide 7: Finding Information
  102. [On Screen Text]
  103.  
  104. Please proceed to Part B and Part C of the lecture.
  105.  
  106. [concluding music]
  107.  
  108. © University of Waterloo
  109.  
  110.  
  111.  
  112. Transcript: Finding Information - Part B
  113. Scene 1
  114. In my PD 20 course, I was advised to consider several questions when searching for information.
  115.  
  116. What are my information needs?
  117. How do I start looking?
  118. What resources do I have access to?
  119. How do I vet sources?
  120. How do I properly cite sources?
  121. Scene 2
  122. Okay, so I should ask myself: “What information do I need?” “What exactly am I looking for?” If I’m not sure, then the first thing to do is figure out what I am looking for.
  123.  
  124. Scene 3
  125. Once I have determined what my information need is, I should write down some keywords or technical terms that I can use to initiate my search. If I only have a vague idea of what to search for, I can do a quick Google search to see if Wikipedia has anything relevant to my search. If I am lucky, I will be able to use Wikipedia to refine my list of keywords or technical terms to search. Though using Wikipedia as my final source is probably not the best idea, I can use it initially to find other sources of information. Checking the sources in the Wikipedia article is also a good way to further information. I should refine my search by figuring out what I can eliminate from my search.
  126.  
  127. Scene 4
  128. I remember that the library had some really good sources on searching. I should refer back to those links for videos to help in my search.
  129.  
  130. Scene 5
  131. So besides the library, what other resources are available to me?
  132.  
  133. Maybe I should start searching for information within this company? Some companies have their own libraries, or they may use Sharepoint (or an equivalent) to store information like training manuals or company best practices. And I should talk to Jon. He is the person who everyone goes to if they have a question about how things are done in the company. He has been with the company for a long time and has the longest institutional memory.
  134.  
  135. Now, what else? Oh, Open-Online Resources: These resources include, but are not limited to, government websites, open access journals, professional forums or the websites of other companies.
  136.  
  137. And if I can’t find the information for free online, it may be the case that there is a fee-for-access. Proprietary Resources are what they are called, but whatever I do, I need to ask my supervisor before committing myself to paying for information.
  138.  
  139. And finally there are Academic Resources. As a UWaterloo student, I have access to all library resources at UWaterloo. In some cases I will need to access the resource through a proxy-server and I will need my UWaterloo credentials to do this.
  140.  
  141. Scene 6
  142. It isn’t enough to find the information I am looking for. The information has to be good and come from a reliable source. The librarians at UWaterloo propose using the C.R.A.A.P. test for vetting information: Currency, Relevance, Authority, Accuracy, and Purpose.
  143.  
  144. Currency: Do I require current information, or will older sources work?
  145. Relevance: Does the information fulfill my information needs? Is the information too elementary or too advanced for my needs?
  146. Authority: What are the author’s credentials and qualifications? Is there contact information?
  147. Accuracy: Has the information been peer reviewed? Are there other sources of information that I can use to corroborate the accuracy of the information?
  148. Purpose: Is the information based on fact or expert opinion? Is the purpose of the information to inform, teach, sell, or persuade?
  149. Scene 7
  150. There is one last important step: keeping track of where I get my information from, and citing it when necessary.
  151.  
  152. Keep track of where I find information as I find it. It seems like a hassle, but it will save me time in the long run. If I write it down the first time, I won’t have to go back and re-search my sources. I have spent lots of time going back to try and find resources when I could have just recorded it the first time. I should make sure that I know the citation style that I want to use to cite my work because it makes it easier to track the resources and put them in my report.
  153.  
  154.  
  155.  
  156. © University of Waterloo
  157.  
  158.  
  159.  
  160. Transcript: Finding Information - Part C
  161.  
  162.  
  163. Finding Information
  164. As an engineering co-op student, you have an obligation to your company, your peers, and the public to be as careful and as thorough as time allows when searching for information. Sometimes the first hit on Google will suffice for your information needs, but there are other strategies to ensure you do your due diligence in the search for information.
  165.  
  166. Here are 5 questions to ask when searching for information.
  167.  
  168. What are my information needs?
  169. How do I start looking?
  170. What resources do I have access to?
  171. How do I vet sources?
  172. How do I properly cite sources?
  173. Asking the right questions is an important first step.
  174.  
  175. Question 1: What are my information needs?
  176. Ask yourself: “What information do I need?” and “What exactly am I looking for?” If you are not sure, then the first thing to do is figure out what you are looking for. Here are some example questions to ask:
  177.  
  178. Do I need a price quote?
  179. Do I need to find instructions?
  180. Do I need to find out about government regulations?
  181. Do I need to find out about industry standards or best practices?
  182. Do I need to find a statistic?
  183. Do I need to find a patent?
  184. This list is obviously not exhaustive. But it should give you a sense of what questions to start with; asking the right questions initially is an important first step.
  185.  
  186. Question 2: How do I start looking?
  187. Once you have determined what your information needs are, write down some keywords or technical terms that you can use to initiate your search. If you only have a vague idea of what to search for, do a quick Google search to see if Wikipedia has anything relevant to your search. If you are lucky, you will be able to use Wikipedia to refine your list of keywords or technical terms to search. Though using Wikipedia as your final source is probably not the best idea, you can use it initially to find other sources of information. Look though the citations used in whatever Wikipedia article you find.
  188.  
  189. Further refine your initial search by figuring out what you can eliminate from your search.
  190.  
  191. Videos
  192. The first video is on how to choose the right keywords for effective results:
  193. Basic Searching 1: Brainstorming Your Research Topic (http://www.lib.uwaterloo.ca/user_ed/basicsearching1.html)
  194. The second video describes the initial steps in searching a database to get more specific results for your information needs:
  195. Basic Searching 2: Searching in a Database (http://www.lib.uwaterloo.ca/user_ed/basicsearching2.html)
  196. The third video describes how to refine your search to get fewer results that are relevant to your information needs:
  197. Basic Searching 3: Refining Your Results (http://www.lib.uwaterloo.ca/user_ed/basicsearching3.html)
  198. Question 3: What resources do I have access to?
  199. Company Resources:
  200. One place to look for information is within your company itself. Some companies have their own libraries, or they may use Sharepoint (or an equivalent) to store information like training manuals or company best practices. And more than likely, there is a go-to person in your company. This is the person who everyone goes to if they have a question about how things are done in the company. They have most likely been with the company for a long time and have the longest institutional memory.
  201.  
  202. Open Online Resources:
  203. These resources include, but are not limited to: government websites, open access journals, professional forums, or the websites of other companies.
  204.  
  205. Proprietary Resources:
  206. If you can’t find the information for free online, it may be the case that there is a fee-for-access. Whatever you do, ask your supervisor before you commit yourself to paying for information.
  207.  
  208. Academic Resources:
  209. As a UWaterloo student, you have access to all library resources at UWaterloo. In some cases you will need to access the resource through a proxy-server. You will need your UWaterloo credentials to do this.
  210.  
  211. Each Engineering department has their own subject guide which is maintained by a Librarian who acts as a liaison for your particular department.
  212.  
  213. Library Resources
  214. Your liaison librarian is there to answer any questions you have about the library and the resources available to you.
  215.  
  216. Chemical Engineering (http://subjectguides.uwaterloo.ca/chemeng)
  217. Civil & Environmental Engineering (http://subjectguides.uwaterloo.ca/civileng)
  218. Electrical and Computer Engineering (http://subjectguides.uwaterloo.ca/eleccompeng)
  219. Management Sciences (http://subjectguides.uwaterloo.ca/manag)
  220. Mechanical & Mechatronic Engineering (http://subjectguides.uwaterloo.ca/mecheng)
  221. Systems Design Engineering (http://subjectguides.uwaterloo.ca/content.php?pid=96268&sid=720749)
  222. UWaterloo has a powerful search engine, Primo Central. Primo Central is an index that provides credible, relevance-ranked results from many of the Library’s online and print collections in a single search.
  223.  
  224. Primo Central (http://primo.tug-libraries.on.ca/primo_library/libweb/action/search.do?vid=WATERLOO&reset_config=true)
  225. Question 4: How do I vet sources?
  226. C.R.A.A.P.
  227. It isn’t enough to find the information you are looking for. The information has to be good and come from a reliable source. The librarians at UWaterloo propose using the CRAAP test for vetting information: Currency, Relevance, Authority, Accuracy, and Purpose.
  228.  
  229. Currency: Do you require current information, or will older sources work?
  230. Relevance: Does the information fulfill your information needs? Is the information too elementary or too advanced for your needs?
  231. Authority: What are the author’s credentials and qualifications? Is there contact information?
  232. Accuracy: Has the information been peer reviewed? Are there other sources of information that you can use to corroborate the accuracy of the information?
  233. Purpose: Is the information based on fact or expert opinion? Is the purpose of the information to inform, teach, sell, or persuade?
  234. Question 5: How do I cite information that I use?
  235. There is one last important step: keeping track of where you get your information from, and citing it when necessary.
  236.  
  237. Keep track of where you find information as you find it. It may seem like a hassle, but it will save you time in the long-run. If you write it down the first time, you won’t have to go back and search for your sources again.
  238.  
  239. Determine which style of citation is to be used. Consistency is the most important thing. If you aren’t told which style to use, or are told to pick whichever style you want, pick one and apply it consistently.
  240.  
  241. Citation Styles
  242. There are different styles of citation: APA, MLA, Chicago, and IEEE to name a few:
  243.  
  244. APA Formatting and Style Guide (https://owl.english.purdue.edu/owl/resource/560/01/)
  245. MLA Formatting and Style Guide (https://owl.english.purdue.edu/owl/resource/747/01/)
  246. Chicago Manual of Style (https://owl.english.purdue.edu/owl/resource/717/01/)
  247. If you are in Electrical and Computer engineering, you will likely use the standard for IEEE:
  248.  
  249. IEEE-SA Standards Style Manual (https://development.standards.ieee.org/myproject/Public/mytools/draft/styleman.pdf)
  250.  
  251.  
  252. © University of Waterloo
  253.  
  254.  
  255.  
  256. Transcript: Monitoring the System
  257. Slide 1: Monitoring the System
  258. [introductory music]
  259.  
  260. Welcome to Unit Three. This unit is on biases that trip us up when we think about information and evidence.
  261.  
  262. Slide 2: Information
  263. I want to begin by telling you the story of McArthur Wheeler. It’s an old story. But it’s a good one. In 1995, Mr. Wheeler robbed two banks, on the same day, in broad daylight, with no visible attempt to disguise his face. Police, with the help of the surveillance tapes and tips from the public, quickly found Mr. Wheeler and arrested him. Mr. Wheeler expressed surprise at the police’s ability to identify him so quickly. It turns out that Mr. Wheeler believed that his face would be invisible to the surveillance cameras if he wiped lemon juice on his face. One has to ask: What led him to such an obviously false belief?
  264.  
  265. Slide 3: Information
  266. Did you ever use lemon juice when you were kid to write invisible messages? It is simple and fun. You dip a Q-Tip, or a quill if you want to go old-school in lemon juice, write a message on a piece of white paper, and let the juice dry. To reveal the message, you simply expose the paper to a heat source.
  267.  
  268. Slide 4: Information
  269. According to reports, Mr. Wheeler knew this fun fact about lemon juice. He hypothesized that the invisible-making properties of lemon juice would also hide his face. So he proceeded to wipe lemon juice on his face. To check that the juice worked, he took a picture of himself with a Polaroid camera. Remember, this was 1995. There were no cell phones with cameras. According to reports, Mr. Wheeler later explained to police that his face had not shown up in the photo.
  270.  
  271. Slide 5: Information
  272. Upon hearing this story, you may have shaken your head in disbelief, rolled your eyes at his simplicity, or simply thought to yourself: I would never do something this dumb. Perhaps not. But before we move on, confident in our intelligence and abilities, let’s take a look at how Mr. Wheeler arrived at his unfortunate conclusion. He started with something that is true. You can in fact hide messages using lemon juice. The message is invisible until it is exposed to heat. From this fact he hypothesized that lemon juice will hide his face from surveillance cameras. He then tested his hypothesis and his test confirmed his hypothesis. The image of his face did not show up in the Polaroid photo.
  273.  
  274. Slide 6: Information
  275. The reality is, we reason like this every day. Here’s a simple example. Fact: the line at the Tim Horton’s in South Campus Hall is sometimes annoyingly long; too long for me to want to line up. But there are times when there is no line. My hypothesis is that there is a small window between the start of class and the end of class where there is no line. If I go between 15 and 25 minutes after classes have started, there is never a line. My hypothesis has been confirmed every time I go to the Tim Horton’s in South Campus Hall.
  276.  
  277. Slide 7: Information
  278. A moment’s reflection should convince you that Mr. Wheeler and I have reasoned in exactly the same way. So how is it that he can be wrong and I can be right? The problem with this question is that it presupposes that I am right. The reality is, my explanation of the behaviour of lines at Tim Hortons could be wrong. In fact, I could be wrong in exactly the same way that Mr. Wheeler was wrong. Mr. Wheeler did not collect enough of the right kind of information to justify his conclusion that lemon juice would hide his face. Likewise, the observations that I have made may not be enough to support my belief about when I should go to Tim Hortons. Let’s unpack what this means.
  279.  
  280. Slide 8: Information
  281. We cannot make good decisions and draw reasonable conclusions without the right kind of information. It would be nice if we were naturally inclined to impartially gather information. But we are not. Our intuitions, prior beliefs, and rules of thumb that we have developed have a profound effect on how much time we spend gathering information. Now, for the most part, these intuitions and rules of thumb serve us well. But, at times, our biases can trip us up. So what is a bias?
  282.  
  283. Slide 9: Biases in Gathering Information
  284. A bias is simply a disposition that we have; a tendency that leads us to a skewed endpoint in reasoning. I want to talk about biases that affect how we gather information.
  285.  
  286. Slide 10: Inattentional Blindness
  287. I am assuming that by now you have watched the Gorilla-on-the-court video. Did you see the guy in the Gorilla suit? If you didn’t, don’t feel bad. You are in the majority of people who, when focused on counting passes, miss the guy altogether. What explains this? Psychologists call this phenomenon Inattentional Blindness. Psychologists have demonstrated over and over again that when normal people are focused on doing normal things, it is quite likely that they will not notice unusual events — even if these events happen right in front of them.
  288.  
  289. Slide 11: Inattentional Blindness
  290. Another example of Inattentional Blindness comes from a study out of Western Washington University. For this study, researchers had a polka-dot-dressed clown ride a unicycle in a busy square on campus. Researchers wanted to see which pedestrians would notice the clown. It turns out that the people who were least likely to see the uni-cycling clown were people talking on their cell phone. Just 25 per cent of the people talking on their cell phone saw the clown cycling around the square. Now this says something about us. It says something about our inability to focus on one task and to remain cognizant of our surroundings.
  291.  
  292. Slide 12: Inattentional Blindness
  293. It is easy to find news stories where inattentional blindness trips people up. A man walking and texting in Los Angeles almost walked into a black bear that was roaming the streets. A woman in Reading, Pennsylvania tripped and fell into a water fountain at a mall. She had been walking and texting. A street in London, England now has padded lampposts. This is to minimize the injuries of people who run into the lampposts while walking and texting.
  294.  
  295. Slide 13: Inattentional Blindness
  296. Sadly, not all texting stories are humorous and light-hearted. Back when Pharrell’s song Happy was still a hit, a woman posted on Facebook saying how much she loved the song. She was driving and had just heard the song on the radio. Because of the distraction, she lost control of her vehicle and crashed. She was killed. There are too many stories like this one. Perhaps you’ve never run into a lamppost. Or fallen into a water fountain. Or walked into a bear. Or been in an accident caused by texting. So how does this apply to the workplace? Here are a couple of scenarios.
  297.  
  298. Slide 14: Inattentional Blindness
  299. You are meeting with your supervisor and two other coop students. Your supervisor is sitting on one side of the table, the three of you are sitting on the other side. Your supervisor is giving you detailed instructions of what she wants done by the end of the day. You are diligently writing down the instructions when you see something out of the corner of your eye. You look at your co-worker’s computer and see a Not-Safe-for-Work post on reddit. You are momentarily taken aback by the brazen behaviour of your peer. You want to say something, but think better of it. Now is not the time or the place. You turn your attention back to your supervisor only to hear her say, “And that’s about it. Any questions?” You realize you have completely missed the last part of her instructions.
  300.  
  301. Slide 15: Inattentional Blindness
  302. Here is another example. Your supervisor and you are conducting a time-sensitive experiment in a lab. The sequence of steps must be timed just right. You are in the midst of the experiment when your supervisor tells you he needs to step out for a second. He instructs you to let him know when the timer dings. You look over. There are four minutes left on the timer. You patiently wait, and when the timer dings, you walk over to the door and poke your head out. You see your supervisor looking in your direction. You tell him the timer dinged. Your supervisor looks at you, but doesn’t say anything. You assume he heard you, so you step back into the lab. A couple minutes pass and your supervisor hasn’t returned, so you go looking for him. You find him sitting at the desk eating a candy bar. He hadn’t heard you.
  303.  
  304. Slide 16: Inattentional Blindness
  305. Think back to the experiment from Western Washington University. Remember the statistic: only 25 per cent of the people who were talking on their cell phone saw the clown cycling around the university square. Let’s say you have a friend who just read the report and claims that they never miss anything when they are on their cell phone. They even marshal evidence: they have never tripped into a fountain, walked into a lamppost, been in an accident. They even boldly claim that they can text during staff meetings at work and not miss a thing. What should you say to your friend? Here is what I would say: if you are blind to the fact that you have missed something (because of inattentional blindness), how can you accurately judge that you haven’t missed anything? You can’t.
  306.  
  307. Slide 17: Biases in Gathering Information
  308. We have a dual burden. Not only are we liable to miss things, we often fall prey to the Attentional Bias — a type of Confirmation Bias. This is a bias which affects the degree to which we examine and remember evidence.
  309.  
  310. Slide 18: Attentional Bias
  311. Now let’s examine my claim that the best time to go to Tim Horton’s in South Campus Hall is between 15 and 25 minutes after lectures begin; if you go then, there will be no line. Given what you know now about attentional bias, you should immediately question how I gathered evidence to support my claim.
  312.  
  313. Slide 19: Biases in Gathering Information
  314. The second type of confirmation bias that I want to talk about is Interpretive Bias. The basic idea is that an interpretive bias affects the significance we assign to the evidence we examine.
  315.  
  316. Slide 20: Interpretive Bias
  317. As an example of this I want to take us back to the Challenger disaster of 1986. On January 28th, 1986, the Space Shuttle Challenger lifted off from the Kennedy Space Center. Tragically, the vehicle broke apart 73 seconds into the flight. It was later determined that the cause of the disaster was a failure of an O-ring in one of the solid rocket boosters. This failure allowed hot combustion gases to leak out, leading to a chain of events which culminated in the disintegration of the vehicle.
  318.  
  319. Slide 21: Interpretive Bias
  320. One of the lead investigators of the incident was Richard Feynman, a well-respected American physicist. Part of Feynman’s investigation looked at the culture of risk taking in NASA. Feynman asked a very simple question: What are the chances of a vehicular failure and the subsequent loss of human life? Feynman discovered that engineers had assessed the risk to be one in one hundred: so if you launch one shuttle a day for 100 days, you can expect to lose one vehicle. Management, however, assessed the risk as one in one hundred thousand. So, from the point of view of management, you can send the shuttle up each day for roughly 300 years and you could expect to lose one vehicle.
  321.  
  322. Slide 22: Interpretive Bias
  323. This is a huge discrepancy. The engineers and management had all the same data in front of them. So how was it possible for management to assess the risk much lower than the engineers? Feynman concluded that it was the managements’ interpretation of the evidence that led them to give a much lower assessment of risk.
  324.  
  325. Slide 23: Interpretive Bias
  326. It was known at the time that gas would leak out when the O-rings failed. It had happened on previous flights, but no previous flight had resulted in tragedy. Management, according to Feynman, took this to be evidence of success. But, as Feynman points out, this is exactly the wrong conclusion: “...erosion and blow-by are not what the design expected, they are warnings that something is wrong. The equipment is not operating as expected.” Feynman’s point is this: You design an O-ring to stop leaks. If the O-ring is leaking, there is something wrong, and you need to fix it. He says further, “The fact that this danger did not lead to a catastrophe before is no guarantee that it will not the next time.” Getting away with it in the past doesn’t mean that you will get away with it in the future. This is a very clear instance of an Interpretive Bias that led to a tragic end.
  327.  
  328. Slide 24: Biases in Gathering Information
  329. The third type of confirmation bias that I want to talk about is called a Structural Bias. The basic idea is that this bias affects the degree to which evidence is made available to us.
  330.  
  331. Slide 25: Structural Bias
  332. Have you ever wondered what it’s like at the bottom of the ocean? Here is how the author of an Economist article summarizes the typical attitude towards creatures living on the bottom of the sea: The ocean floor is a domain of exile. It is the place species remain when they have been pushed out of intensely competitive shallow-water environments. Then, when waves of extinction rock the planet, such banished animals vanish and their places are filled by another set of losers from the shallows. A typical explanation for why this is the case sounds entirely plausible. Yes, there are fewer resources at the bottom of the ocean. But there are also fewer species with lower population densities, which makes the struggle for scarce resources less fierce, which in turn allows species which are no longer evolutionarily viable on the surface to persist.
  333.  
  334. Slide 26: Structural Bias
  335. As the author of the article points out, this evidence is rather one-sided. It is one-sided because it is exceedingly difficult to study fossils at the bottom of the ocean. In other words, the received view is born of a structural bias. Without access to fossils, any evolutionary explanation about how animals end up down there is tenuous at best. If you’re wondering, scientists have recently found a deep-sea fossil field in a gorge in the Alps. They have uncovered thousands of fossils from 68 different species. With this they are able to challenge the orthodox view that the ocean floor is for evolutionary losers.
  336.  
  337. Slide 27: Biases in Gathering Information
  338. We have discussed four biases that affect how we collect and examine information. It is very easy to be tripped up by these biases. In fact, they are structured in such a way so as to evade detection. Even if we are aware of them, it doesn’t guarantee that we will catch them in action. This is called a bias blindspot. I hinted at it in the first unit. Even if we are aware that we are prone to error, we can quickly convince ourselves that we are not, at this moment, making a mistake. Why?
  339.  
  340. Slide 28: Blindspot Bias
  341. We are storytellers, and we like stories that are consistent. And when it comes to gathering information and evidence, we have a tendency to stop looking when we are able to tell a consistent story. Note that I am using the word story very broadly here and mean to include things like theories and explanations. Go back to the example of Mr. Wheeler. Mr. Wheeler was convinced that lemon juice would hide his face from security cameras. He even tested his theory. He took a picture of his face with a Polaroid camera, and the photo came out blank. To Mr. Wheeler’s mind this confirmed his theory. The information he gathered from the photo was consistent with his belief lemon juice has invisible-making properties. So why bother doing any more testing? And this is where the confirmation bias tripped him up. He should have done more testing. He should have taken more pictures of his face with different types of cameras. He should have looked at himself in the mirror and asked, why can I still see my face? The confirmation bias convinced him that he had all the information he needed.
  342.  
  343. Slide 29: How to Guard Yourself
  344. Everyone is susceptible to the same type of error that Mr. Wheeler made. And if you think otherwise, that is likely because you have fallen prey to the confirmation bias. So what are some ways to avoid being tripped up?
  345.  
  346. 1. Insert yourself into a community of skeptics. This can be as simple as frequenting different websites committed to checking facts and debunking common myths and misconceptions. Or perhaps you can surround yourself with friends and colleagues who have developed good behaviours, like fact checking, asking obvious questions, and examining issues from different perspectives.
  347.  
  348. Slide 30: How to Guard Yourself
  349. 2. Listen to the naysayers. It is easy to let group-think take over when working collaboratively. So when working in a group, be sure to listen to what the naysayer has to contribute. This is not easy to do. If you think something is so painfully obvious, and should be obvious to anyone with a brain, it is very easy to dismiss the person who insists on asking pesky questions.
  350.  
  351. Slide 31: How to Guard Yourself
  352. 3. Question your assumptions. Ask yourself questions like: What would need to happen for me to be wrong? Under what conditions would I be wrong? What will happen if I am wrong? What information/evidence am I overlooking? Finally, who can I ask for impartial feedback?
  353.  
  354. Slide 32: Biases in Gathering Information
  355. Learning about biases and taking steps to route the potential biases in our thinking is a lot of work; I will grant you that. But no one ever said self-betterment was easy. If they did, they were trying to sell you something. I’m here to tell you that your hard work will pay off. You will become a valuable person to have around and this will undoubtedly engender future success. Remember this: biases affect how we collect and examine information.
  356.  
  357.  
  358.  
  359. © University of Waterloo
  360.  
  361.  
  362.  
  363. Transcript: Reasoning with Evidence
  364. Slide 1: Reasoning with Evidence
  365. [introductory music]
  366.  
  367. Welcome to Unit Four. Up to this point in the course we have been talking about gathering information: admitting when we don’t know how to find reliable information and the importance of avoiding biases that affect how we gather and interpret information. We are now in a new section on analytical reasoning. In this section, we talk about arguments and how to reason with evidence.
  368.  
  369. Slide 2: Analytical Reasoning
  370. What is analytical reasoning? Each one of us has beliefs about the world. We have beliefs about who is going to win the Stanley Cup this season. We have beliefs about prime numbers. We have beliefs about the King of France. We have all sorts of beliefs. The question is, where do these beliefs come from and how are they formed? It would be nice if our beliefs were the result of explicit reasoning and argumentation, but they are not.
  371.  
  372. Slide 3: Analytical Reasoning
  373. Quite often we just have beliefs. We are influenced by our friends, family, religion, politics, everyday experience, and more often than not, our beliefs escape critical evaluation. Think of analytical reasoning as a process of stepping back and analyzing our beliefs. Think of it as a type of quality control, and our goal is to root out those beliefs which don’t pass inspection.
  374.  
  375. Slide 4: Analytical Reasoning
  376. One of my pet peeves is when somebody uses the that’s-just-your-opinion line on me. I find this particularly annoying when I have made a legitimate claim that is supported by compelling reasoning. It’s not that I overestimate my ability to debate other people. What bothers me is that I am never quite sure what the other person means. They could just be making a political claim, which is fine. Or, they could be dismissing the importance of evidence and sound reasoning, which is not fine.
  377.  
  378. Slide 5: Analytical Reasoning
  379. From a political point of view, all opinions are equal in the sense that everyone is entitled to their own opinion. Everyone is free to express themselves as they see fit. No one can be silenced, even if their opinion is uninformed, unreasonable, or even offensive. This right is enshrined in the Canadian Charter of Rights and Freedoms. But this is from a political point of view. From a logical point of view, not all opinions are equally good.
  380.  
  381. Slide 6: Analytical Reasoning
  382. Consider the following scenario. Assume that Smith and Jones are having a debate. And assume that nobody’s opinion is wrong. So let’s say Smith makes the following claim: “All opinions are correct.” To which Jones replies, “Your opinion is wrong.” What do we make of this?
  383.  
  384. Slide 7: Analytical Reasoning
  385. If Smith is correct, then Smith must agree that Jones is correct, which is to say that Smith is wrong. In other words, in claiming all opinions are correct, Smith has contradicted himself. He has committed himself to a self-refuting position. So no, not all opinions are equally good. Some opinions are self-refuting and, as such, we don’t have to give much thought to them. But a contradiction isn’t the only grounds for rejecting a claim.
  386.  
  387. Slide 8: Analytical Reasoning
  388. I’ve said numerous times already that many of our beliefs remain unexamined. When our beliefs are challenged, we must provide justification for what we believe. We cannot expect others to believe what we believe simply because we believe it. Opinions which are supported by evidence and sound reasoning are more acceptable than opinions which are not.
  389.  
  390. Slide 9: Analytical Reasoning
  391. Remember what the PEO Code of Ethics says. When it comes to matters of engineering, Professional Engineers are expected to discourage untrue, unfair or exaggerated statements. Further, it is expected that Professional Engineers will not express opinions on matters of engineering that are not founded on adequate knowledge and honest conviction.
  392.  
  393. Slide 10: Analytical Reasoning
  394. We do not want bridges built by engineers who believe pi is 3.2 (you say no one could possibly believe that? Then google The Indiana Pi Bill, 1897). We do not want oil refinement processes designed by chemical engineers who whole heartedly embrace the theory of phlogiston (a theory of combustion from the 1700s). Yes, these are preposterous examples, but don’t miss the underlying point. Engineers cannot just say whatever they want on matters of engineering. Engineers have collectively agreed that every individual engineer has an obligation to support their opinions with evidence and sound reasoning. When an engineer expresses their opinion on matters of engineering, they must provide reasons to justify their opinions.
  395.  
  396. Slide 11: Analytical Reasoning
  397. But this does not mean that everyone will be in agreement about everything. There will always be differences of opinions. Even if we are in a community of practice where opinions are supported with evidence and sound reasoning, people can still—quite legitimately—disagree with each other. Even if we are working with the same information and evidence, we may have different opinions about what the evidence means. Evidence, by itself is uninteresting. It is what we say about it, and the conclusions that we draw from it, that matter. And this is where the process of argumentation comes into play. People will expect you to justify your opinion. Providing an argument that is clear and convincing is one way to do so.
  398.  
  399. Slide 12: Analytical Reasoning
  400. The word argument conjures up negative connotations. We associate arguments with something that is emotionally charged and highly divisive; with raised voices, red faces, and the slamming of doors. But that is not how I am using the word argument. An argument is simply a set of premises that are given in support of a conclusion. There’s nothing aggressive or confrontational about this. It is simply an intellectual process.
  401.  
  402. Slide 13: Analytical Reasoning
  403. The premises of an argument are declarative sentences. A declarative sentence says something about the world. It is the type of thing that is true or false. So a premise can be a statistical claim, an evidential claim, or a report about data; it can even be a theoretical claim, a definition, or an axiom. Again, a premise is a sentence — either true or false — which purportedly tells us something about the world.
  404.  
  405. Slide 14: Analytical Reasoning
  406. We use the information in the premises to arrive at some other claim, which we will call the conclusion. It too, is a declarative sentence. And that is all an argument is. It is the process of using information, data, evidence (or whatever) to justify our belief that some other claim — the conclusion — is true.
  407.  
  408. Slide 15: Analytical Reasoning
  409. Consider the following example. All uWaterloo students are diligent students. Sanket is a uWaterloo student. Hence, Sanket is a diligent student. This is a straightforward argument. It’s fairly easy to identify the conclusion: Sanket is a diligent student. The word hence is what gives it away. The other two sentences are the premises.
  410.  
  411. The first premise is all uWaterloo students are diligent students. The second premise is Sanket is a uWaterloo student. For an argument to be compelling, the conclusion should follow from the premises. That is, if it is reasonable to believe the premises are true, then it should be reasonable to believe the conclusion is true as well. In this particular case, if both premises are true, then the conclusion must also be true.
  412.  
  413. Slide 16: Analytical Reasoning
  414. We will talk about how to evaluate arguments in Unit 6. For now, I want to talk about two different types of arguments: deductive arguments and ampliative arguments. Deductive arguments are, in some sense, truth preserving. A deductively sound argument is one which takes us from true premises to a true conclusion. If the premises are true, the truth of the conclusion is guaranteed. How can we be so sure? Because the information in the conclusion does not extend beyond the information in the premises. The conclusion simply makes explicit what the premises already tell us. Consider the following example.
  415.  
  416. Slide 17: Analytical Reasoning
  417. Premise 1: All cats are animals. Premise 2: Boots is a cat. Conclusion: Hence Boots is an animal. If we agree that Boots is a cat (she is) and if we agree that all cats are animals (they are), then the truth of the conclusion is forced on us. We cannot, on pain of contradicting ourselves, deny that Boots is an animal. The conclusion follows from the premises. All the information we need to conclude that boots is an animal is in the premises. There is no inferential leap. The conclusion tells us something new in the sense that it makes explicit what is already known.
  418.  
  419. Slide 18: Analytical Reasoning
  420. Here is another example to illustrate the point. Let’s say I draw a hand from a standard deck of 52 playing cards. You didn’t actually see me draw them, so you don’t know how many cards are in my hand. I look at you and say, only one of the following claims is true.
  421.  
  422. My hand contains a king, an ace, or both;
  423. My hand contains a queen, an ace, or both;
  424. My hand contains a jack, a ten, or both.
  425. I then look at you and say, if you can guess which claim is true, I will give you 5 bonus marks. (Keep in mind that this is only a hypothetical). As you are about to say something, I look at you and say, “You know, I kind of like you, so I’m going to make it easier for you. I will eliminate one of the possibilities: I have an ace in my hand.”
  426.  
  427. [On Screen Multiple Choice Question]
  428.  
  429. What are the chances that I have an ace in my hand?
  430.  
  431. 0
  432. 1/6
  433. 1/2
  434. 2/3
  435. 1
  436. Slide 19: Analytical Reasoning
  437. Let’s break this down in terms of information. This is what you know: A. I have cards in my hand. B. That only one of the three claims is true. C. That I told you I have an ace in my hand. Do you have good reason to suspect I am lying to you?
  438.  
  439. Slide 20: Analytical Reasoning
  440. Let’s assume my assertion in (C) is true: I do have an ace in my hand. This means that claim (1) and claim (2) are both true. But from (B) you know that only one of the claims can be true. And this is a contradiction. So our assumption that I have an ace in my hand must be false. Our assumption has led to an absurdity. This form of argument is called reductio ad absurdum. This is a Latin phrase which literally means reduction to the absurd. It is a powerful form of deductive reasoning. When I lied and told you that I had an ace in my hand, you had all the information you needed to catch me in my lie. You didn’t need any more information.
  441.  
  442. Slide 21: Analytical Reasoning
  443. Deductive reasoning is very powerful. If we are confident that the information we are dealing with is reliable and complete, we can be confident in the conclusions we arrive at using valid deductive forms of reason. (We will talk more about deductive forms of reasoning in Unit 6). But even if the information we do have is reliable, it is not always possible to avoid making inferential leaps. Sometimes we find ourselves working with incomplete information. This is where we must use ampliative reasoning.
  444.  
  445. Slide 22: Analytical Reasoning
  446. The conclusion of a deductive argument does not give us new information. In contrast, the conclusion of an ampliative argument gives us more information than what is in the premises. Hence, the truth of the conclusion of an ampliative argument is not guaranteed. Of course the goal is to have a conclusion which is true, but sometimes we get things wrong. This is just to say that an ampliative argument is defeasible. Even if we have made all of the right observations, it is possible that our conclusion is wrong. So instead of saying the premises — if true guarantee the truth of the conclusion, we say that the premises of an ampliative argument, if true, lend credence to the conclusion.
  447.  
  448. Slide 23: Analytical Reasoning
  449. So what value is there in ampliative reasoning if it has the potential to lead us from true premises to a wrong conclusion? The fact of the matter is, ampliative arguments account for most of our reasoning. Anytime we are engaged in evidential reasoning, scientific reasoning, or reasoning about cause and effect, we are making ampliative arguments. Once we start extrapolating from our experiences, we move beyond what is known and make inferences about what is not known.
  450.  
  451. Slide 24: Analytical Reasoning
  452. An example of ampliative reasoning is inductive reasoning, which is the move from observed cases to a general claim about unobserved cases. Here’s an example. Let’s say you work as quality control for a company that makes widgets. You don’t have the time or resources to check every widget, but you are able to check a few. The first widget you check passes the test. The second widget you check is also good. Now let’s say you do this eight more times. What conclusions can you draw from your next observation? You’ve observed ten good widgets. Are you justified in concluding that the 11th widget will be good as well? It’s important to note three things. First, your observations count as confirming evidence. And this evidence lends credence to the conclusion. This evidence provides us with reasons for believing the next widget will meet quality standards. The second thing to note is the conclusion goes beyond observed cases and makes a positive statement about an unobserved case. The third thing to note is that all the evidence is the same type of thing: you are only concerned about the quality of widgets, and so you only look at the widgets, and then make a conclusion about unobserved widgets.
  453.  
  454. Slide 25: Analytical Reasoning
  455. For as tenuous as inductive reasoning appears at first blush, we use it on a daily basis. Inductive reasoning persuades us that the ground beneath us will not collapse in a sink hole when we take our next step. It is what convinces us that the 8-ball will move in a predictable way when we strike it with the cue-ball. It is what gives us confidence that the world will still be there when we open our eyes after a nap.
  456.  
  457. Slide 26: Analytical Reasoning
  458. A second example of ampliative reasoning is called inference to the best explanation. Inference to the best explanation helps us make sense of what we have observed. Our stated hypothesis helps us tie all of the evidence together in a coherent way. Have you ever heard the expression: If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck. That, in essence, is inference to the best explanation.
  459.  
  460. Slide 27: Analytical Reasoning
  461. Let’s say I’m walking around Laurel Creek conservation area and hear quacking. A little while later, I reach the shoreline and look over the water. I see something with striking duck-like features, floating on the water. It then starts to swim. There are two possible explanations for what I have seen and heard. 1. What I’ve seen and heard is actually a duck. 2. I am being tricked by a kid with an animatronic duck and call. Both hypotheses are consistent with what I have observed, but assuming it is an actual duck is a simpler explanation. Why would somebody go to all the trouble to build an animatronic duck, put it in the lake, and then stand on the shore with a duck call? It is easier to believe that it is a real duck.
  462.  
  463. Slide 28: Analytical Reasoning
  464. But let’s say the skeptic in me awakens. I want to confirm that it is a real duck. So I walk along the shoreline to see if I can find a kid with a duck call and remote control. If I don’t find anyone fitting that description, that is more evidence that the thing on the water is a real duck. And the assumption that it is a real duck accounts for that evidence. I could, further, throw rocks in the direction of the duck (not at the duck, of course!), with the hopes of scaring it into taking flight. That, again, would be more evidence. And again, the assumption it is a duck accounts for the new evidence. But notice my conclusion that it is a real duck is based on different types of evidence. Inference to the best explanation is simply the process of formulating a hypothesis which does the best job of accounting for the different types of evidence. It is consistent with the evidence. It helps us make sense of the evidence, and it helps explain why the evidence exists.
  465.  
  466. Slide 29: Biases in Gathering Information
  467. We cannot expect people to accept what we say simply because we say it. It doesn’t matter how confident we are or how loudly we say it. People will expect you to support what you say with evidence, with data, and with current research. But evidence does not speak for itself. Evidence must be interpreted; it is what we say about it, and the conclusions that we draw from it, that matter. Providing an argument that is clear and convincing is one way to fulfill the expectations others have that you will justify your opinion.
  468.  
  469.  
  470.  
  471. © University of Waterloo
  472.  
  473.  
  474.  
  475. Transcript: Understanding Evidence
  476. Slide 1: Understanding Evidence
  477. [introductory music]
  478.  
  479. Welcome to Unit 5. We are now at the halfway mark of the course.
  480.  
  481. Slide 2: Analytical Reasoning
  482. Last unit we began a section on analytical reasoning. People will not believe what we say simply because we believe it or say it. They will expect us to give reasons and justify our opinions. I proposed last unit that providing an argument which is clear and convincing is a way to fulfill that obligation. A good argument will convince people that our opinions are justified and worth considering. As I explained, an argument is simply a means by which we move from premises — evidence, information, data, etc — to a conclusion.
  483.  
  484. Slide 3: Analytical Reasoning
  485. A good argument is one where the conclusion follows, in some sense, from the premises. But this means different things for different types of arguments. Remember the distinction I made between deductive arguments and ampliative arguments. A sound deductive argument will take us from true premises to a true conclusion. Although deductive arguments tell us something new about the world (they make explicit what is contained in the premises) there is no inferential leap. A sound deductive argument preserves truth. If the premises of a deductively valid argument are true, the truth of the conclusion is guaranteed.
  486.  
  487. Slide 4: Analytical Reasoning
  488. By contrast, the conclusion of an ampliative argument expresses information that goes beyond what the premises tell us. Our premises may all be true, but our conclusion might be false. In other words, ampliative arguments are, in principle, defeasible. We may have collected all the right evidence, but there is no guarantee that the conclusion we draw is correct. This is not to discount the value of ampliative reasoning. Ampliative arguments can be incredibly powerful and convincing, if we do it right. But there will be times when we must make decisions with incomplete information. Our goal is to give the best arguments and make the best decisions with the information we have. The problem is, we do not like incomplete information. And when we are faced with incomplete information, we have a tendency to fill in the blanks with details that may or may not have anything to do with the situation. Consider the following example.
  489.  
  490. Slide 5: Incomplete Information
  491. Mia is 27 years old, single, outspoken, and smart. While in university she majored in English Lit and minored in Philosophy. She was also an ardent supporter of gay and lesbian rights, and picketed a department store that did not accommodate nursing mothers. Given what we know about Mia, which of the following statements is most likely true? A. Mia is an active feminist; B. Mia is an active feminist who works in a bookstore; C. Mia is an active feminist who works in a bookstore and volunteers at a local woman’s shelter. So, what do you think is most likely true: A, B, or C?
  492.  
  493. Slide 6: Incomplete Information
  494. If you chose C, you are not alone. The answer in C grabs our attention, not because it’s the most probable, but because it’s the most interesting. Of the three answers, answer A is the most probable. If you want to see the mathematics behind this claim, look in the extra resources section for this unit. But answer A is the least interesting. That’s why we pass over it so quickly. Each one of us is a story teller and the temptation is to opt for what makes for an interesting story over that which is more probable. Answer C fits well with assumptions that are consistent with the information that we’re given about Mia. When we read about Mia, we’re more than willing to fill in the details, even embellish the details. We do so to help us make sense of the information that we’re given. The problem is, what often emerges is a caricature. What often happens is that we get caught up in our story and the conclusions that we come to are consequently less likely true.
  495.  
  496. Slide 7: Analytical Reasoning
  497. The point is, we must keep track of the information we have and the assumptions we make to ensure the conclusion we draw is actually reasonable. But drawing the right conclusions from the evidence we have can be a tricky business. This is because a body of evidence rarely leads to one, clear, definite conclusion. The evidence we have may support multiple conclusions.
  498.  
  499. Slide 8: Multiple Conclusions
  500. Here’s an example. Say you have a bag with apples and oranges in it and you ask me to guess how many apples and oranges you have. You tell me that you have at least one apple and at least one orange. You also tell me that you spent five dollars and that apples cost one dollar each and that oranges cost two dollars each. Is there any way that I can deduce how many apples you have and how many oranges you have. Well, no, there is not. Why? Because there are two possible conclusions. You either have two oranges and one apple or you have one orange and three apples. There are multiple conclusions that I can draw, all of which are consistent with the information that I have.
  501.  
  502. Slide 9: Multiple Conclusions
  503. This example is simple, but the point is not. It is easy to fall into a confirmation bias if we do not acknowledge the possibility that multiple conclusions can follow from the same evidence. If you fixate on one conclusion and ignore all others, you are caught in a confirmation bias. So consider all plausible conclusions and resist discounting a conclusion until there is good reason — evidence — to do so. And if you find yourself in a situation where multiple conclusions are consistent with the evidence, be sure to take additional steps to put yourself in a position where you can decide between them.
  504.  
  505. Slide 10: Multiple Conclusions
  506. Here is an example from the history of science that beautifully captures how the same phenomena can be explained by two very different — even incompatible — hypotheses. If you have ever had the opportunity to go out at night and observe the night sky, you will notice that most heavily bodies — the moon, the planets, and the stars — appear to rise in the east and move westward over the course of the night. Now if you pay close attention you will notice the planets move in a way that the stars do not. The planets move relative to the background stars. Not only that, every once in a while it appears as though a planet will stop in its tracks and reverse its direction, only to stop again and start moving in its original direction. This phenomena is called the retrograde motion of the planets. The ancient astronomers knew about the retrograde motion of the planets, but they had a really difficult time explaining it due, in part, to their view of the universe.
  507.  
  508. Slide 11: Multiple Conclusions
  509. The ancients held to a geocentric model of the universe. The earth, they thought, was at the centre of the universe. The moon’s orbit was the closest to earth, which was followed by Mercury’s. The sun’s orbit lay between Venus and Mars. Jupiter’s orbit was just beyond that of Mars. Saturn was the furthest planet from the earth. And beyond Saturn lay the sphere of the stars.
  510.  
  511. Slide 12: Multiple Conclusions
  512. Claudius Ptolemy — who was born at the end of the first century A.D. — was the first astronomer to give an explanation of the retrograde motion of the planets. Ptolemy hypothesized that planets orbited in epicentres within their circular orbits around the earth. This hypothesis accounted for the retrograde motion of the planets, and allowed Ptolemy to explain what many astronomers before him could not. Not only that, Ptolemy’s model allowed astronomers to make predictions about the movement and locations of the planets.
  513.  
  514. Slide 13: Multiple Conclusions
  515. We now know that Ptolemy was wrong. The planets and sun do not orbit around the Earth. The planets are not on circular orbits. And the planets do not orbit on epicentres. Our heliocentric model of the solar system is a much better model, and does a much better job explaining the retrograde motion of the planets. Yet when it was posited by Galileo in the 16th century, the heliocentric model was not well received by the establishment. In fact, the theory was outright rejected and Galileo was condemned for it. Even with new empirical evidence garnered from technological advancements like the telescope, people still held on to the old theory, another example of confirmation bias at work.
  516.  
  517. Slide 14: Multiple Conclusions
  518. But don’t lose the point. The point is, both Ptolemy’s geocentric theory and Galileo’s heliocentric theory explain the retrograde motion of the planets. There were, in effect, two competing — and incompatible — theories which explain the phenomena. The heliocentric theory eventually won out, but it took some time for the new theory to unseat the old.
  519.  
  520. Slide 15: Using Evidence
  521. Now despite the fact that confirmation bias hangs over our heads like a spectre, we can — and should — use new evidence to bolster our beliefs and opinions. We just have to be very clear and explicit about our assumptions, beliefs, and methodologies. Here is an example.
  522.  
  523. Slide 16: Using Evidence
  524. Let’s say I invite you to play a simple game involving a coin flip. If you give me 50 cents, I will flip a coin. If the coin lands Tails, I give you $1. If the coin lands Heads, I keep your 50 cents and you get nothing. Now you think to yourself, This sounds like a fair bet. And you are right, because your expected utility of this game is 50 cents. Here is the calculation. The probability of heads equals the probability of tails equals one half. Expected utility equals the probability of tails times a dollar plus the probability of heads times zero dollars which equals fifty cents.
  525.  
  526. Slide 17: Using Evidence
  527. But just before you commit yourself to playing the game with me, one of your friends runs up to you and pulls you aside. They tell you they have good reason to believe that the coin I use is biased to land Heads 60 percent of the time. You ask your friend why they think that. They tell you they played the game earlier and lost a lot of money. You consider what your friend told you, but you figure there is only a 5 percent chance that I would be dishonest and use an unfair coin. You take me to be a mostly honest person and decide to play the game with me anyways.
  528.  
  529. Slide 18: Using Evidence
  530. So we start playing. And wouldn’t you know it, the first 5 flips come down Heads. So you ask yourself, What are the odds of that happening? If the coin is fair, the objective chance of flipping 5 Heads in a row is 0.5 × 0.5 × 0.5 × 0.5 × 0.5. Which equals 0.031. So with a fair coin, it is roughly a 3% chance that you will see 5 Heads flipped in a row. But what if your friend is right about the coin being Heads-biased? If the coin I have is biased to land Heads 60 percent of the time, the objective chance of flipping 5 Heads in a row is 0.6 × 0.6 × 0.6 × 0.6 × 0.6. Which equals 0.078. So with a Heads-biased coin, it is roughly an 8% chance that you will see 5 Heads flipped in a row.
  531.  
  532. Slide 19: Using Evidence
  533. Now is a good time for you to reconsider your decision to play the game with me. The pertinent question for you is, what are the chances the coin is biased? You have observed the coin land Heads five times in a row. What you want to know is if the coin is biased. We can figure that out by using Bayes’ Theorem. I am going to throw a lot of math at you. But don’t worry about that and just stick around for the moral of the story.
  534.  
  535. Slide 20: Using Evidence
  536. If you are correct in your assessment of me—that there is only a 5% chance that I’m cheating—we can use that information to calculate the conditional probability of flipping 5 Heads in a row if the coin is biased. So we multiply the chances that I am a cheater (0.05) by the chances of getting five heads in a row (0.078) and get 0.004. We can do a similar calculation to determine the conditional probability of flipping five heads in a row if the coin is fair. In which case we multiply the chances that I am not a cheater (0.95) by the chances of flipping five heads in a row (0.031) and get 0.030. So what do these numbers tell us? Nothing yet. But just wait.
  537.  
  538. Slide 21: Using Evidence
  539. Bayes’s theorem tells us to focus on the two ways of flipping 5 Heads in a row. Specifically, it tells us to add those two probabilities together. Which gets us: 0.034. We then take the probability of getting five heads in a row if the coin is biased (0.004) and divide that by our new sum (0.034). Which gets us 0.116, or 11.6 percent. And that’s the important number to pay attention to. Don’t worry about the mathematics for now. If you want, there is supplementary material in the unit landing page that gives a robust account of Bayes’ Theorem. But for now, focus on the moral of the story.
  540.  
  541. Slide 22: Using Evidence
  542. Here is the moral. I offered to play a game with you that involved flipping a coin. Your friend warned you that the coin was Heads-biased. You decided there was only a five percent chance that I would use an unfair coin. Where did this five percent come from? That number simply represents your degree of belief. The higher the number, the stronger your belief I am a cheater.
  543.  
  544. Slide 23: Using Evidence
  545. Since you didn’t really think I’m a cheater, you decided to play the game. You then observed me flip five heads in a row. You then asked yourself, what are the chances Andres is using a biased coin? By applying Bayes’ Theorem, you get the answer: 11.6 percent. In other words, the new evidence you have — seeing five heads land in a row — gives you more reason to believe I am a cheater. Your degree of belief moves from 5 percent to 11.6 percent. The point is, you can use new evidence to update your prior beliefs.
  546.  
  547. Slide 25: Using Evidence
  548. But doesn’t this fly in the face of what I have been saying about the confirmation bias? Not really. It is important to pay attention to new evidence. But it is equally important not to let the new evidence get the best of us. Yes, you have more reason to believe I am a cheater, but there is still roughly an 88 percent chance that I am not a cheater. In other words, it is very likely that there is an alternate explanation for seeing five heads land in a row. Multiple conclusions can follow from the same evidence. The key is not to fixate on evidence or one particular explanation.
  549.  
  550. Slide 26: Using Evidence
  551. We will not always have all the information that we want or need. Our goal must be to give the best arguments and make the best decisions with the information we have. To do this, we must use evidence correctly; we must keep our assumptions in check, and we must consider all possible explanations.
  552.  
  553.  
  554.  
  555. © University of Waterloo
  556.  
  557.  
  558.  
  559. Transcript: Evaluating Inferences
  560. Slide 1: Evaluating Inferences
  561. [introductory music]
  562.  
  563. Slide 2: Conditional Reasoning
  564. You are walking towards the lunchroom with one of your peers. You are talking about the upcoming mid-term reviews. Your peer confidently asserts that they have nothing to worry about: Listen, if I’m doing my job well, our boss won’t call me into his office to reprimand me. Since she hasn’t called me into her office to reprimand me, I must be doing my job well! This feels wrong. But you don’t know quite how to articulate the error your co-worker has made.
  565.  
  566. Slide 3: Conditional Reasoning
  567. You arrive at the lunchroom only to be met by an animated discussion between two other colleagues: Okay, there are two and only two options: it either happens or it doesn’t happen. Hence, the chances are 50-50! Sigh. Wanting to eat your lunch in peace, you walk to the end of the table where no one else is sitting. You sit down. You begin eating your lunch and notice someone has left an internal memo on the table. Curious, you read it.
  568.  
  569. Slide 4: Conditional Reasoning
  570. Our existing best-practices are not robust enough to prevent a wide-spread chemical spill. That’s not all. In the unfortunate event there is a chemical spill, our existing emergency protocol will not prevent significant environmental damage. And if our company is responsible for causing damage to the environment, we will be caught up in a negative media storm. So if we do not want negative press, we need to review and rewrite our best practices. Something about this argument makes you feel uncomfortable: how can you get from taking about wanting to avoid bad press to talking about the need to revise best practices? Here are some tools and techniques to help you analyze different types of arguments.
  571.  
  572. Slide 5: Conditional Reasoning
  573. Let’s begin by analyzing four different types of conditional reasoning: two are valid, the other two are invalid. The first valid form of reasoning is Modus Ponens — affirming the antecedent. Premise 1 If Boots is a cat, then Boots is an animal. Premise 2 Boots is a cat. Hence, Boots is an animal. The underlying logical form is straightforward: If P, then Q, P, therefore Q. This is a valid form of deductive reasoning. Which simply means if the premises are true, the truth of the conclusion is guaranteed. This doesn’t mean that every conclusion will be true. Rather, if the conclusion of a Modus Ponens argument is false, that just means one of the premises must be false as well.
  574.  
  575. Slide 6: Conditional Reasoning
  576. The second valid form of reasoning is Modus Tollens — denying the consequent. Premise 1 If Boots is a cat, then Boots is an animal. Premise 2 Boots is not an animal. Hence, Boots is not a cat. This should fit with our intuitions: being an animal is a necessary condition for being a feline. Again the underlying logical form is straightforward: If P, then Q. Not-Q, hence not P. Being valid, this form of argument will never take us from true premises to a false conclusion. Modus Ponens and Modus Tollens are common forms of valid reasoning, and you have no doubt encountered them at some point. But it is important to keep them distinct from two similar forms of conditional reasoning which are invalid.
  577.  
  578. Slide 7: Conditional Reasoning
  579. The first is called denying the antecedent. Here is an example: Premise 1. If sub-atomic particles go faster than the speed of light, then the general theory of relativity is wrong. Sub-atomic particles do not go faster than the speed of light. Hence, the general theory of relativity must be right. For as alluring as the conclusion is, the underlying form of reasoning is invalid. That is, this form of reasoning will take us from true premises to a false conclusion. Here is a clear counterexample.
  580.  
  581. Slide 8: Conditional Reasoning
  582. Premise 1: If Boots is a cat, then Boots is an animal. Premise 2: Boots is not a cat. Hence, Boots is not an animal. This is clearly wrong. Even if Boots isn’t a cat, she can still be an animal. There are other types of animals after all. Perhaps she is a dog. The underlying form of argument is: If P, then Q. Not P, hence, not Q. Again, this is an invalid form of argument. Even if the premises are both true, the truth of the conclusion is not guaranteed.
  583.  
  584. Slide 9: Conditional Reasoning
  585. Now consider this argument: Premise 1 If I have read this map correctly, the next intersection should be King and Erb. Premise 2 And yes, it is King and Erb. Hence, I must have read the map correctly. You have seen this form of argument already this lecture. Remember the argument we started off with: If I’m doing my job well, our boss won’t call me into her office to reprimand me. Our boss hasn’t called me into her office to reprimand me. Hence, I must be doing my job well. This form of reasoning is called affirming the consequent. It is an invalid form of reasoning. Which simply means it does not preserve truth. It can take us from true premises to a false conclusion.
  586.  
  587. Slide 10: Conditional Reasoning
  588. Here is a clear counterexample. Premise 1 If I’m the richest man in the world, then there won’t be any bears around me. There aren’t any bears around me. Hence, I must be the richest man in the world. Both premise one and premise two are true. I’m not sure who the richest person in the world is, but I can assure you it is not me. This is the method of counter-example. And now you have the conceptual tools to elegantly demonstrate to your peer why he is wrong in thinking that he has nothing to worry about because your boss hasn’t called him into her office yet.
  589.  
  590. Slide 11: The method of counter example
  591. If someone gives you an argument and you think there’s something fishy about it, try and come up with an argument that has the same logical form where the premises are obviously true but where the conclusion is obviously false. If you are successful, you have demonstrated that the argument is invalid. That is, you have demonstrated the form of argument can take us from true premises to a false conclusion.
  592.  
  593. Slide 12: Alternate Explanations
  594. A fairly common view of science is that we start by making some initial observations. Once we have made these observations, we then state a tentative hypothesis that accounts for what we have observed. We then test the hypothesis with further observations to confirm whether we have things right. But notice the underlying form of argument: If P, then Q. Q, therefore P. This is an instance of affirming the consequent, which, we have just demonstrated, is an invalid form of reasoning. But this does not mean we should reject this form of scientific reasoning. What it does mean is that we must understand exactly where we can go wrong with this underlying form of reasoning.
  595.  
  596. Slide 13: Alternate Explanations
  597. We run into problems when we focus on one explanation to the exclusion of other, plausible, explanations. Remember back to Unit Three where we discussed the three types of confirmation bias: attentional bias, interpretive bias, and structural bias. All three biases, in some form or other, arise when we only look at evidence that proves us right. We must not simply look to evidence which confirms our hypothesis. What we must do, instead, is to think about what would refute our hypothesis and then look to see if those conditions are true.
  598.  
  599. Slide 14: Looking for alternate explanations
  600. Consider the following example. Let’s say Ken has volunteered to be a test subject in a psychology experiment. In this particular test, Ken is given two series of numbers: 2, 4, and 6, and 8, 10, and 12. The experimenters ask Ken if he can figure out the general principle behind the two series of numbers. The answer the experimenters are looking for is: a series of three numbers that differ by two. Ken’s answer however is, a series of three numbers ascending by two. Now, let’s say Ken can test his hypothesis by proposing additional series of numbers. The experimenters will tell Ken whether the numbers are consistent with the correct general principle. So, Ken offers the following series of numbers to test his hypothesis: 14, 16, 18 and 20, 22, 24. The experimenter tells Ken that both series are consistent with the correct general principle. Both tests confirm Ken’s hypothesis that the general principle is a series of three numbers ascending by two. And therein lies the problem with Ken’s test. He made a hypothesis and he tested his hypothesis, but he only looked to confirming instances of his hypothesis. His observations are correct and his proposals are consistent with the right answer, but his conclusion is wrong.
  601.  
  602. Slide 15: Looking for alternate explanations
  603. What Ken could do instead, is offer different series of numbers. For example: 5, 3, 1 or 2, 4, 2. If Ken had given these series, the experimenter would have told Ken that both series were consistent with the correct general principle. And Ken would have known that his initial hypothesis was wrong and could have taken corrective measures. Ken, however, fixated on confirming evidence and missed the opportunity to give the right answer. The point: Always look for alternate explanations. Yes, I keep coming back to this point, but I do so because it is an important one.
  604.  
  605. Slide 16: Strong but Wrong Intuitions
  606. I want to return to one of the examples I gave at the beginning of the lecture; the one with the inference: It either happens or it doesn’t, so the chances of it happening are 50-50. Sometimes what we take to be common sense isn’t reliable. We can see why this inference is wrong with the Monty Hall problem.
  607.  
  608. Slide 17: Strong but Wrong Intuitions
  609. Let’s say you are a contestant on a gameshow and you are playing for a sports car. You know the sports car is behind one of the three closed doors in front of you. If you choose the door with the car behind it, you get to keep the car. Suppose you choose door one. What are your chances of being right? Clearly it’s one in three. There are three doors and you choose one of them.
  610.  
  611. Slide 18: Strong but Wrong Intuitions
  612. Now Monty — the host — turns to you and says, before I open the door you chose, I’m going to generously open one of the other doors. I know that the car is not behind door three. I’ll even open it up to show you. So, he opens up door three. True to his word, there is no car behind it. Monty must be in a really generous mood, because he then says to you, Now, I’m going to let you change your mind. If you want, you can choose door two. What should you do? Should you change your mind and choose door two? Would choosing door two make any difference?
  613.  
  614. Slide 19: Strong but Wrong Intuitions
  615. What are the chances that your first choice is the right choice? We know the car is either behind door one or door two. But does that mean you have a fifty-fifty chance of being right? Well, this is where our intuitions lead us astray. Many people, when they are confronted with this problem for the first time, reason in the following way: There are two doors. Since I’ve chosen door one, it’s either the right one or it’s not. So, it’s a fifty-fifty chance that I’ve chosen the right door. So, no, there’s no point in changing strategies. If I switch to door two, it’s still a fifty-fifty chance that I’m right. But this is the wrong way to think about the situation. Although there are only two options — door one and door two — it does not follow that the probability of door one being correct is 50 per cent and the probability of door two being correct is 50 per cent. If we don’t keep our intuitions in check, we will be led astray.
  616.  
  617. Slide 20: Strong but Wrong Intuitions
  618. To see why, ask yourself what the probability is that you’ve chosen the wrong door. We know the probability of some event A occurring plus the probability of event A not occurring equals one. With some basic algebra, we know the probability of event A not occurring is equal to the difference between one and the probability that event A occurs.
  619.  
  620. Slide 21: Strong but Wrong Intuitions
  621. So in our example the probability your original choice is wrong is two out of three. Two of the other doors could have the car behind them. So, the probability that the car is behind doors two or door three is two out of three. But we know the car is not behind door three. So, the probability that the car is behind door two is two out of three. Given what we know, the best thing to do is change strategies. Choose door two. The chances of winning increased from roughly 33 per cent to roughly 66 per cent.
  622.  
  623. Slide 22: Strong but Wrong Intuitions
  624. The point: when reasoning from evidence to a conclusion, it is imperative that we keep our intuitions in check and pay attention to new, relevant, information. The rules of thumb we rely on can lead us from good evidence to a bad conclusion.
  625.  
  626. Slide 23: Interpreting Data
  627. How we interpret new information can have a profound effect on the conclusions we make. Consider the following example.
  628.  
  629. Slide 24: Interpreting Data
  630. Shadman is working for a software company during his co-op term. The company — Aspirational Learning — designs and builds online corporate training modules. The company listens very closely to their clients and uses their clients’ feedback to improve their product. Over the past few years the most common feedback was the modules need to be more interactive and engaging. To address this, Aspirational Learning has just rolled out a new set of modules, with the key feature being gamification. Participants now learn through playing games. Many of the games are played online with other participants. The modules are accessible across all mobile platforms.
  631.  
  632. Slide 25: Interpreting Data
  633. One of their largest clients — Geriatric Inc — has recently expressed concern over the efficacy of the new learning modules: the participation rates of its employees are low, the performance scores of their employees are below expectations, and the overall feedback from their employees is negative. Here are the numbers. Participation rates 55.4 per cent; performance scores 71.1 out of a hundred; feedback 7.7 out of ten.
  634.  
  635. Slide 26: Interpreting Data
  636. These numbers are concerning, and not wanting to lose their biggest client, Aspirational Learning appoints a task force to investigate how to improve the value of the customer’s experience. Shadman is tasked with looking for an explanation of the numbers. It doesn’t take Shadman very long to come away with some interesting findings. If you look at the numbers for participants under 50, you get a very different picture. Numbers increase across all categories. Participation rates jump from around 55 per cent to over 93 per cent. Average performance scores increase by 4. And the feedback scores increase from 7.7 to a score of 8.1.
  637.  
  638. Slide 27: Interpreting Data
  639. Shadman finds the numbers change again if he further narrows the scope of the participants to those 35 years old and under. Participation rates jump to 100 per cent, average performance scores are now in the low 90s, and feedback is at a very positive 9.7 out of 10.
  640.  
  641. Slide 28: Interpreting Data
  642. Shadman tentatively concludes that the modules resonate with the younger participants in a way that it does not with the older participants. To verify his hunch, Shadman decides to divide participants into distinct age categories and then compare numbers. Here are his results. 51 to 68 year olds, participation rates 8 per cent; performance scores 13 out of a hundred; feedback 1.5 out of 10. For 36 to 50 year olds, 88.9 per cent for participation rates; performance scores are 67.5 out of a hundred and feedback is 6.9 out of 10. 21 to 35 year olds participation rate 100 per cent; performance scores 93.6; feedback out of 10, 9.7.
  643.  
  644. Slide 29: Interpreting Data
  645. What is the point of this example? Numbers do not speak for themselves. New information requires interpretation. And how we interpret the information can have a profound effect on the conclusions we make.
  646.  
  647. Slide 30: Interpreting Data
  648. Given the original data set, it would be tempting to conclude that the new modules are of no value and that Aspirational Learning had completely missed the mark. However, with the analysis in hand, it looks like a very different conclusion is warranted. The modules work exceptionally well for the younger employees. So in that regard, Aspirational Learning completely gets the younger market. However, the same cannot be said for their understanding of the older generations.
  649.  
  650. Slide 31: Interpreting Data
  651. It is not enough to simply have reliable evidence and a strong data set. Data does not speak for itself. Evidence needs an interpretation. Confidence in the conclusions we make comes from evaluating the evidence and examining the underlying form of reasoning.
  652.  
  653.  
  654.  
  655. © University of Waterloo
  656.  
  657.  
  658. Transcript: Knowing It vs. Showing It
  659. Slide 1: Knowing It vs. Showing It
  660. [introductory music]
  661.  
  662. Welcome to Unit Seven.
  663.  
  664. Slide 2: Knowing It
  665. So far in this course we’ve talked about different types of knowledge, reflective thinking, deductive reasoning, and reasoning with evidence. Unit Seven is the beginning of a new section that focuses on professional communication. This might seem like a dramatic shift in focus, but there’s a very good reason for it.
  666.  
  667. Slide 3: Explicit Knowledge
  668. Remember back to Unit One when I talked about knowing what you don’t know. I spent quite a bit of time talking about two different states of ignorance that we can find ourselves in: not knowing that you don’t know, and knowing that you don’t know. But I didn’t spend much time talking about the first quadrant of the Jahari window: the state where you know that you know something. All I said at the time was that this knowledge is explicit knowledge. It’s the type of knowledge that you can articulate. You’ll be able to teach and explain what you know to other people. And therein lies the motivation for this section on professional communication. You may have the most brilliant idea in the world, perhaps one that will even make you lots of money. But if you are unable to clearly communicate your ideas to the right person in the right context and in the right way, nothing will come of your ideas.
  669.  
  670. Slide 4: Conclusions vs Recommendations
  671. So what do we want to communicate? One of the things you will be asked to communicate in the workplace are recommendations. We have spent a lot of time so far in this course talking about how to arrive at reasoned conclusions. Recommendations are not the same thing as conclusions. Here is an article — written tongue-in-cheek — from the Economist. Consider what the author writes.
  672.  
  673. Slide 5: Conclusions vs Recommendations
  674. Humans may soon have to look to their laurels as the planet’s dominant species. Turkeys, heretofore harmless, have been exploding in size, swelling from an average 13.2lb in 1929 to over 30lb today. On the fairly scientific assumption that present trends will persist, The Economist estimates that turkeys will be big as humans in just 150 years. Before 6,000 years are out, turkeys will dwarf the Earth itself.
  675.  
  676. Of course, the conclusion is wrong. It is wrong because the author wrongly assumes that present trends in the growth of turkeys will continue indefinitely into the future. But don’t let the cheekiness of the article distract you. Just focus on what the conclusion is. The author continues: Scientists and industry experts claim that the burgeoning of turkeys is largely the result of selective breeding. The fact that their growth is artificial, and that most have lost the ability to fly, suggest that all is not lost. Still, with nearly 250 million turkeys gobbling in America, the only prudent course of action this holiday season is to eat them before they eat you. What is the author’s recommendation?
  677.  
  678. Slide 6: Conclusions vs Recommendations
  679. The author’s conclusion is that turkeys will dwarf the earth itself in 6000 years. That’s the conclusion. Their recommendation is that we should eat the turkeys before they eat us. As I mentioned earlier, a recommendation is not the same thing as a conclusion. One thing to note is that there can be multiple recommendations stemming from the same conclusion. Going back to the turkey example, another recommendation would be: we should stop selectively breeding turkeys for size. We now have two recommendations coming from one conclusion.
  680.  
  681. Slide 7: Conclusions vs Recommendations
  682. It is also important to note that it is possible to derive mutually exclusive recommendations from the same conclusion. A third recommendation could be: we should stop eating turkeys. Clearly the first and third recommendations are mutually exclusive. You can’t both eat turkeys and not eat turkeys at the same time. This isn’t to say that there is something wrong with our conclusion. All this does is highlight the fact that the conclusions and recommendations are different things.
  683.  
  684. Slide 8: Conclusions vs Recommendations
  685. The difference between a conclusion and a recommendation is a difference in kind. The difference rests on the distinction between the descriptive and the normative. A descriptive claim describes something factual, something empirical. A normative claim is a claim about what should be done.
  686.  
  687. Slide 9: Conclusions vs Recommendations
  688. For example, consider the claim: the tests indicate circuits A and B were destroyed in the electrical storm. This is a descriptive claim about the way the world is. It is a fact and is true just when circuits A and B were — in fact— destroyed in an electrical storm. A normative claim is a claim about what should — or ought to — be the case. Both circuits are destroyed, so we should replace both circuits. A conclusion is some fact about the world. A recommendation is a claim about what we should do in response.
  689.  
  690. Slide 10: Framing Information
  691. Consider this example. Let’s say you are working for the regional health district in northern Ontario. A serious outbreak has occurred in a remote village of 600 people. All 600 residents of the village are sick and require immediate access to medication. You have done a quick inventory and concluded that the health district only has enough medication for 200 people. What’s worse, it will take over a week for more medication to be flown in, which will be too late for the other 400 people. So what should be done? You are put on a task force to come up with two recommendations. You are given four hours to come up with a plan.
  692.  
  693. Slide 11: Framing Information
  694. After a very stressful 4 hours, you and your team have come up with two recommendations. The first recommendation is to give a full dose to 200 people. Those 200 people will live; the other 400 people will die. Or, the second recommendation is to give a partial dose to all 600 people. But this is a risky strategy as there is a 2/3 chance that everyone will die. You need a full dosage for the medication to do its thing. So there is one conclusion: there is only enough medication for 200 people. And there are two recommendations: give a full dosage to 200 people, or, give a partial dosage to all 600 people.
  695.  
  696. Slide 12: Framing Information
  697. Some of you will have noticed that the expected utility is the same for both recommendations. In both cases, 200 people are expected to live and 400 people are expected to die. And you might be wondering why? Because how you frame your recommendations is crucially important.
  698.  
  699. Slide 13: Framing Information
  700. When this thought experiment was first done by Daniel Kahneman and Amos Tversky, 72 per cent of those surveyed chose the first recommendation—to give 200 people a full dosage; and 28 per cent chose to give a partial dosage to everyone. Those surveyed should have been indifferent between the two recommendations. So what’s going on here? Were the participants acting irrationally? Not necessarily, because we do not focus on the numbers. Our focus is on something else. Consider what happened when the outcomes of the thought experiment changed.
  701.  
  702. Slide 14: Framing Information
  703. The setup and recommendations remained the same. What changed was how the consequences were framed. If a full dosage is given to 200 people, the other 400 people will die. If a partial dosage is given to all 600 people, there is a 1/3 chance that no one dies. Notice again that the expected utility of both recommendations is the same. However, when the same participants were asked which recommendation they would vote for, 22 per cent said that they would favour giving 200 people a full dosage, and 78 per cent said that they would favour the course of action where there was a 1/3 chance that no one would die.
  704.  
  705. Slide 15: Framing Information
  706. The difference between the first pair of recommendations and the second pair of recommendations is how loss and gains are described. In the first instance, the first recommendation results in a sure gain, whereas the second recommendation results in the probability of a complete loss. In the second scenario, the first recommendation results in a certain loss, whereas the second recommendation results in the probability of a full gain. As Kahneman and Tversky explain, we are risk averse and tend to take a certain gain over a probable loss. Not only that, but when faced with a certain loss, we are much more willing to take the option with a probable gain.
  707.  
  708. Slide 16: Framing Information
  709. The underlying point is crucially important: how we frame recommendations can affect how people respond to them and influence their subsequent choices. It may be tempting to use this knowledge to influence decisions in your favour. Just remember, though, people generally do not respond well when they find out you have manipulated them. Consider this example.
  710.  
  711. Slide 17: Framing Information
  712. Let’s say it is time for my yearly performance review. As part of my review, I must demonstrate strong teaching. But the only data I have to demonstrate this is by teaching evaluations. The teaching evaluations for Arts comprises nine questions ranked on a Likert scale from 1 through 5 — with 5 being the highest. So let’s say I compile the averages for my courses in the past year and put it in graph form. I have done this in Chart 1.1. At first glance, it looks as though my teaching has improved greatly: the first course starts out pretty low, but I’m topping the charts by the last one. Impressed? I hope not. There are a couple of problems with how I constructed Chart 1.1. For starters, I did not include any information about when I taught the courses. Information pertaining to the order in which I taught the courses is important information. I have included that information in Chart 1.2. This is much less impressive. My teaching started off strong in the fall, but by the time spring rolled around I was clearly phoning it in.
  713.  
  714. Slide 18: Framing Information
  715. The second thing to notice are the numbers in the y-axis in Chart 1.1. Remember the Likert scale in the end-of-course surveys ranges from 1 to 5. The range in Chart 1.1 is from 3.8 to 4.3. This results in a skewed visual effect, as the difference between the highest value and the lowest value is accentuated. Consider what happens visually in Chart 1.3 when we change the range of values to reflect the Likert scale. The difference between the highest and lowest values becomes much less dramatic. But there is no attempt to hide information or influence the interpretation of the reader. The chart has all of the pertinent information, it still portrays a downward trend in evaluations as the year progresses, and it does a much better job capturing the spirit of the Likert scale used in the end of course surveys.
  716.  
  717. Slide 19: Framing Information
  718. So what’s the underlying point? The primary point is this: it is crucially important to frame information in a transparent and responsible way. Do not hide information. And do not try to manipulate the information to make it say something that it doesn’t actually say. A secondary point is this: be very careful when looking at visual representations of data. Pay close attention to how graphs and charts are constructed. You do not want to be duped by someone who is trying to manipulate you.
  719.  
  720. Slide 20: Communicating for Your Audience
  721. So far in this unit we have made the distinction between conclusions and recommendations, we have talked about the importance of framing recommendations and visual information in a transparent and unbiased way. I want to end by briefly talking about adjusting your communication style to your audience. As I said in the beginning, you may have the most brilliant idea in the world, but if you are unable to clearly communicate your ideas to the right people in the right context and in the right way, nothing will come of your idea. You must adjust what you communicate and how you communicate it to who your audience is.
  722.  
  723. Slide 21: Communicating for Your Audience
  724. 1. Assess what your audience knows, what they want to know, and what they need to know. What you communicate to someone who is familiar with your project will be different from what you communicate to someone who has no idea what you’re working on. Similarly, what you communicate to a fellow member on your project will be different from what you communicate to a stake-holder who is not directly involved in your project.
  725.  
  726. 2. Gauge what your relationship is to your audience. How you communicate with a fellow co-op student will be different than how you communicate with your supervisor’s boss. You can probably get away with being informal with your fellow coop student. However, depending on your workplace, it is probably prudent to be more formal while addressing your supervisor’s boss. Similarly, how you communicate with an external stakeholder will be different than how you communicate with someone on your project team. It will be prudent to guard what you say and how you say it when talking with an external stakeholder. Remember: context matters.
  727.  
  728. 3. Determine what level of detail you need to go into. If your supervisor asks for a 30 second update, skip the details and stick to the important big picture stuff. If your project manager asks for a detail account of your day, don’t give her a dismissive everything went well. And finally,
  729.  
  730. 4. Does your audience want the conclusions of your work, or your recommendations? If your supervisor has asked for your recommendations, do not simply give them your conclusions. Recommendations require further thought and analysis. What counts as a reasonable recommendation will be constrained by budgets, time, resources, manpower, etc. Similarly, if your supervisor asks you for the conclusions you made in your work, avoid making recommendations. They do not want to hear what you think should be done. They are most likely just interested in the facts.
  731.  
  732. Slide 22: Communicating for Your Audience
  733. The distinction between conclusions and recommendations is not an intuitive one. The difference between conclusions and recommendations is a difference in kind. Conclusions are factual. They describe what is the case. Recommendations are normative. They are statements about what should be done. Multiple recommendations can be derived from the same conclusion and it is possible for recommendations to be mutually exclusive — where acting on one precludes us from acting on the others. Above all else: be clear and tailor your message to your audience. The distinction between conclusions and recommendations may seem obvious now, but don’t let that trick you into thinking that the distinction is unimportant.
  734.  
  735.  
  736.  
  737. © University of Waterloo
  738.  
  739.  
  740. Transcript: Professional Communication
  741. Slide 1: Professional Communication
  742. [introductory music]
  743.  
  744. Welcome to unit eight. Unit eight is a continuation of the section on professional communication. By the end of this unit, you will have a fairly good idea of why what you say can differ from what you end up communicating.
  745.  
  746. Slide 2: Communication
  747. Consider the following scenario. Wendy meets two of her friends at the theatre. She sees that one of her friends is wearing a new sweater and decides to pay him a compliment: that sweater looks great on you! Before her friend can say thank-you, her other friend retorts, what, and it would look ugly on me? We have all been in similar situations where we have said one thing but others thought we were saying something completely different. This type of situation is frustrating and awkward at best. The question is why does this happen? The short answer is that communication is built upon a set of presupposition, and it breaks down just when these presuppositions fail.
  748.  
  749. Slide 3: Language: A Vehicle of Communication
  750. Let’s say you and I are standing by a window, and I have just nodded in the direction of the window and said, it’s raining. In all likelihood, you would look out the window and say something like, yes, yes it is. Communication happens that fast. But what has actually happened between my initial utterance, it is raining, and your subsequent affirmation? Let’s make it explicit.
  751.  
  752. Slide 4: Language: A Vehicle of Communication
  753. When I say, it’s raining, you think to yourself: He’s made an utterance; There must be a reason he’s made the utterance; He must know what the utterance means; He must have intended to mean it; Since he meant it, he must want me to believe it; It’s unlikely that he wants to deceive me, and it’s unlikely he has mis-spoke; So, it’s probably true. You then look out the window and utter, yes, it is. Of course this multistep process is rarely made explicit. And when it is made explicit, it looks ridiculously obvious. But that is good because then it is more likely that you’ll agree with me when I say communication is a two-way street.
  754.  
  755. Slide 5: Presuppositions
  756. In the one way, communication involves a speaker expressing their thoughts. In the other way, it involves an audience — perhaps an audience of just one — attributing intensions to the speaker in a bid to understand what the speaker means. When someone speaks they presume the hearer is capable of understanding. There’s no point in me trying to communicate with someone if I know that they are not capable of understanding me. It would be pointless and frustrating for me to even try. Secondly, it is presumed that the hearer will recognize the speaker’s communicative intent. When we communicate with each other, we do so in order to relay information. And the speaker assumes that the hearer will pick up on that intent to inform. For example, if you’re walking across the street, and I yell out, look out for that bus, I’m assuming you will recognize that what I’m saying is a warning and act on that information.
  757.  
  758. Lastly, it is presumed that the hearer will take the speaker literally unless there’s a reason to think otherwise. So when I say, it’s raining outside, what I mean is that it’s raining outside — nothing more, nothing less. And when I say, you’re in danger of being hit by a bus, what I mean is that you’re in danger of being hit by a bus — nothing more, nothing less. But the hearer also makes presuppositions. The hearer presumes that the speaker has a communicative intent.
  759.  
  760. So when my wife calls and says, pick up some milk on your way home, I’m assuming she intends to relay information to me. I don’t assume that she simply phoned me to make noise. It’s not in my rational self-interest to assume that. The hearer also presumes that the speaker intends to be understood literally, unless there is reason to think otherwise. So when your friend says to you, I really like your sweater, you should assume your friend really means what they say. Unless there’s reason to think that your friend is being insincere.
  761.  
  762. Slide 6: Presuppositions
  763. The point is, communication is born of tacit assumptions that both speaker and hearer make about each other. That is the nature of communication. You can’t read my mind, and I can’t read yours. One consequence of this is that a speaker may use an utterance to mean one thing, but the hearer may take the utterance to mean something else. What is said isn’t always what is understood, in which case we have a breakdown in communication. Another consequence is that a speaker can use an utterance to mean something more than its literal meaning. A speaker can imply meaning.
  764.  
  765. Slide 7: A Speaker Can Imply Meaning
  766. Language can be used in very subtle ways, especially if meaning is implied. Now, it is important to understand what I mean by the word imply here. The word imply can mean one of two things. It can mean follows as a logical consequence. For example, let’s say the police are investigating a murder. They have collected and evaluated all the evidence. And they conclude that the butler did it. In which case we say the evidence implies that the butler did it. There’s a logical connection between the evidence and the conclusion. Imply can also mean indirect suggestion. Going back to the previous example, let’s say the police talked to the gardener early on in their investigation. They told the gardener, we know the perp wore a size ten shoe. We know that you wear a size ten. To which the gardener would likely retort, are you implying I’m a suspect? The police didn’t explicitly say the gardener was a suspect, but they certainly implied it.
  767.  
  768. Slide 8: Conversational Implicature
  769. Using an utterance that means one thing literally to imply some other meaning is known as conversational implicature. Conversational implicature can take many forms.
  770.  
  771. Slide 9: Conversational Implicature
  772. Innuendo is one such form. An innuendo is an indirect remark that is suggestive or disparaging. So, for example, Bob asks Janet, why did you break up with Doug? To which Janet replies, because I want to date someone who is intelligent. If taken literally, all Janet has said is that she wants to date someone who is intelligent. But that’s not what she means. What she means is that Doug isn’t all that bright; she means it as a disparaging comment about Doug’s intelligence.
  773.  
  774. Slide 10: Conversational Implicature
  775. Or let’s say Doug has asked his boss for a letter of recommendation. His boss tactfully writes, I cannot say enough good things about Doug or recommend him too highly. Taken literally this is a ringing endorsement of Doug’s value as an employee. But there is another way to look at what the boss has says and it’s not very favourable to Doug. Clearly the boss doesn’t think too highly of Doug.
  776.  
  777. Slide 11: Conversational Implicature
  778. Rhetorical questions are another form of conversational implicature. Although rhetorical questions look a lot like real questions, they are not genuine questions. A genuine question is used to elicit information. A rhetorical question, on the other hand, is used to say something. For example, let’s say Dave and Greg are in a heated debate about the rights of animals. Greg boldly asserts that, animals have rights. To which Dave responds, do they? Clearly Dave thinks otherwise. He doesn’t say as much, but the tone of his response indicates he thinks Greg is wrong.
  779.  
  780. Slide 12: Conversational Implicature
  781. Rhetorical questions can also be used to shift the burden of proof in an argument. Let’s say Dave begins by asserting, we can do whatever we want with animals. Now being the kind of guy he is, Greg responds by saying, no, we can’t mistreat animals for no good reason. To which Dave says, can’t we? Who has the burden of proof here? Well, Dave does in so far as he began the conversation with a contentious claim. Dave clearly denies Greg’s claim, but instead of defending his claim, Dave craftily shifts the burden of proof onto Greg with a well-placed rhetorical question. Greg is now on the defensive and Dave is off the hook.
  782.  
  783. Slide 13: Conversational Implicature
  784. Staying with the theme of questions, loaded questions are another form of conversational implicature. A loaded question assumes that a prior unasked question has already been answered. The respondent is then burdened with either granting the questionable assumption or taking it upon themselves to dispel it. In either case the burden is unwarranted.
  785.  
  786. Slide 14: Conversational Implicature
  787. Consider the following example of a loaded question. Let’s say Dave meets Greg on the street. Greg is with his new girlfriend. Dave enjoys making other people uncomfortable so he asks Greg, have you stopped hurting kittens yet? Greg is in a lose-lose situation. He can’t say yes because that implies he used to hurt kittens. And he certainly can’t say no because that implies that he still hurts kittens. He can try to rebut the idea that he hurts kittens, but that will make him look defensive and guilty. Or he can just remain silent and let Dave’s comment slide, but that will just make him look cold and uncaring. Again, Dave’s loaded question has put Greg in a lose-lose situation.
  788.  
  789. Slide 15: Conversational Implicature
  790. Here’s a more serious example. In 2009 New Zealanders were asked to vote in a citizen initiated referendum concerning corporal punishment. Should a smack — as part of good parental correction — be a criminal offence in New Zealand? This is an excellent example of a loaded question, for it forces the respondent to assume that smacking a child is a legitimate form of punishment. So if a respondent answers no to the question, what are they saying no to? Are they saying no to the question is smacking a child a good part of parental correction? Or are they saying no to the question should smacking a child be a criminal offence in New Zealand? Although it is possible to say no to both questions, someone who disagrees with corporal punishment would likely say no to the first question but yes to the second. Similarly, a proponent of corporal punishment would likely say yes to the first question but no to the second. The problem with a loaded question lies in what it forces the respondent to assume. You can’t answer a loaded question without committing yourself to what has been assumed but left unsaid.
  791.  
  792. Slide 16: Conversational Implicature
  793. Sarcasm is another form of conversational implicature. That it is should be immediately obvious. With a subtle change in tone, you can alter the meaning of an expression. Tone, of course, isn’t the only signal of sarcasm, but it is one of the most obvious ones. Though sarcasm can add humour to a situation, be careful when using it in the workplace. Remember that tone is not easily conveyed through emails or text messages. Communication can quickly break down if sarcasm is misunderstood — and working relationships can be strained if sarcasm is misused or used in excess.
  794.  
  795. Slide 17: Conversational Implicature
  796. The last form of conversational implicature that we will look at is the non-denial denial. The term non-denial denial is attributed to a former Washington Post editor who coined the phrase to characterize the uncanny ability politicians and other public figures have for denying allegations without actually denying the allegations. This is how a non-denial denial works. An allegation is made. The accused respond with a denial, but what they said leaves it as a possibility that the allegation is true. The accused don’t actually deny the allegation. They deny something else. They, in effect, change the subject.
  797.  
  798. Slide 18: Conversational Implicature
  799. So listen very closely when a politician or other public figure denies an accusation. They have responded with a non-denial denial if they say, I can’t recall what happened, or they characterize the allegations as outrageous or ludicrous, or they simply discredit or mock the source of the allegation.
  800.  
  801. Slide 19: Conversational Implicature
  802. There are some classic examples of non-denial denials. Richard Nixon declared, I am not a crook, when he denied allegations of involvement in the Watergate Scandal. Bill Clinton brazenly declared, I did not have sexual relations with that woman, when he denied accusations of sexual improprieties in the White House. Mark McGuire declared, I’m not here to talk about the past, when he denied allegations of steroid use. Each of these people denied something, but they avoided denying the actual accusation brought against them. So why go through all the trouble of a non-denial denial? The reason is simple. Because if, in the future, the truth does come out, the person can’t be accused of lying.
  803.  
  804. Slide 20: Presuppositions
  805. We have looked at five ways in which the literal meaning of an utterance can be superseded by a non-literal meaning. Now, I’m not criticizing the general practice of conversational implicature. My only purpose is to draw your attention to the practice and to emphasize the importance of understanding how it can lead to awkward misunderstandings. Recall the presuppositions upon which communication is predicated. The speaker presumes that the hearer will take them literally unless there is good reason to think otherwise. And the hearer presumes the speaker is being literal unless there is good reason to think otherwise. These two qualifications are crucially important. If either speaker or hearer fail to understand what the other is doing, miscommunication is inevitable.
  806.  
  807. Slide 21: Sometimes words just mean what they mean!
  808. The reason we don’t respond to a rhetorical question is because we recognize that the speaker wasn’t eliciting information from us. So if your professor asks a rhetorical question but you raise your hand to answer it, don’t be surprised by your professor’s response.
  809.  
  810. Slide 22: Sometimes words just mean what they mean!
  811. The reason you don’t say thank you when your friend says nice hair in sarcastic tone, it’s because you recognize that your friend isn’t really complimenting you. But if your friend does compliment you on your new hairstyle and you respond as though she’s being sarcastic, be prepared to offer an apology.
  812.  
  813. Slide 23: Conversational Implicature
  814. The cues that a literal meaning is not intended can be very subtle. But remember that, despite all I’ve said here about conversational implicature, sometimes words mean just what they are intended to mean.
  815.  
  816. Slide 24: Biases of Language
  817. Conversational implicature isn’t the only thing that influences what we actually end up communicating. Biases of language and communication also affect how others interpret what we say. Now, allow me to say something obvious. We use words to talk about the world. That’s the power of language. And we take the corresponding mental representations and associate them with other mental representations to form new thoughts and ideas. But here’s the catch. What I’ve just said is so obvious that we don’t really think about this process of association. And if we have a tendency to automatically associate two concepts, that can affect how we view events and interpret what others say to us.
  818.  
  819. Slide 25: Biases of Language
  820. Social psychologists have developed an experimental method for determining how strongly people automatically associate two concepts. It’s called the Implicit Association Test. I’ve included a link to an online implicit association test in extra resources section for this unit. I strongly encourage you to check it out. The results can be very eye opening. The test determines how strongly people associate two concepts by measuring how quickly respondents are able to categorize the concepts when they are split apart. The test is used to determine our automatic responses to people of different races, ethnicity, religion, sexual orientation, political leaning, etcetera.
  821.  
  822. Slide 26: Sometimes words just mean what they mean!
  823. How is this relevant to you as an engineering co-op student? Well, here’s an example. Let’s say that Jennifer is on her first co-op term. She’s studying mechanical engineering and is working in the manufacturing sector. She’s on a team that is responsible for setting up welding equipment on an assembly line. She sees that there are problems with the design of the equipment which will make repeatable quality welds almost impossible. So she raises this concern with her immediate supervisor. Now, let’s say her supervisor associates youthfulness with inexperience. So whenever he interacts with a young person he immediately thinks of them as inexperienced. If he is unaware of this implicit association, he will likely dismiss Jennifer’s concern.
  824.  
  825. Slide 27: Biases of Language and Communication
  826. Why is this an issue of communication? Because implicit association has a profound effect on how information is shared in group contexts. Our automatic responses will affect how we interact and communicate with people of different ethnicities, religions, gender, and sexual orientation. And if the flow of information is impeded, problems will arise.
  827.  
  828. Slide 28: Biases of Language and Communication
  829. Loaded language is another form of bias in communication. We talked earlier about loaded questions. So you may be thinking what’s the difference between a loaded question and loaded language? A loaded question burdens the respondent with an implicit assumption. A loaded question forces the respondent to assent to something that is left unspoken. Loaded language on the other hand entails using evaluative words to evoke an uncritical assent to what is said. Loaded language assumes as correct the very thing it is purportedly trying to demonstrate.
  830.  
  831. Slide 29: Biases of Language and Communication
  832. Consider the following example. Let’s say you’re in a project design meeting. Two feasible design proposals have been tabled by their respective design team. And it’s time to decide between the two proposals. The chair of the meeting stands and says, we have two design proposals to vote on. All in favour of the ridiculous design proposal by team stupid say aye. The use of loaded language is obvious. The call to vote itself maligns the proposal by calling it ridiculous and is intended to influence the outcome of the vote. And no one will want to vote for the team that has just been ostracized. As such the use of loaded language precludes the possibility of a free and fair vote.
  833.  
  834. But what if instead that the chair of the meeting said, we have two design proposals to vote on. All in favour of the first feasible design proposed by the first team say aye. Is the wording of this call to the vote any better? Well, the team is not disparaged, so that’s good. But the word feasible is still used, and feasible is an evaluative word. Remember, loaded language entails using evaluative words to evoke an uncritical assent to what is said. And to say that the design proposal is feasible is to say, among other things, that the proposed design meets the design requirements, and that it does not exceed established constraints. In other words, it does what we want it to do given the limitations we have. But I don’t think the expression feasible design is an instance of loaded language in this case. It is possible to use evaluative language in a non-loaded way. And as long as both design teams were given equal time to explain and defend their proposal then the call to vote is fine. It’s not trying to sway the vote one way or the other in isolation of a reasoned discourse.
  835.  
  836. Slide 30: Communication Rests on Presuppositions
  837. What we say can differ from what we end up communicating. And in the workplace where the sharing of information is crucially important, it is important to understand why. I said at the beginning of this unit, that communication is predicated upon assumptions that both speaker and hearer make about each other. One consequence is that the speaker may mean one thing but the hearer may understand something different. Loaded language, even if it is used inadvertently, will affect the way people respond to what we say. And the automatic and often unconscious associations we make will affect how we share information with people of different ethnicities, religion, gender, and sexual orientation. A second consequence is that an utterance can mean something more than its literal meaning. Innuendo, rhetorical questions, loaded questions, sarcasm, non-denial denials are just a few instances of conversational implicature — the practice of using expressions that mean one thing to say something completely different. The practice of conversational implicature isn’t necessarily a bad thing. However, if we miss that a speaker does not intend to be taken literally, miscommunication is inevitable. We can’t read each other’s minds. The best we can do is guess each other’s intentions. The better we are at signaling our intentions and the better we are at discerning the intentions of others, the better we will be at communicating.
  838.  
  839.  
  840.  
  841. © University of Waterloo
  842.  
  843.  
  844. Transcript: Public vs. Personal Morality
  845. Slide 1: Public vs. Personal Morality
  846. [introductory music]
  847.  
  848. Welcome to unit 09.
  849.  
  850. Slide 2: This Unit
  851. Up to this point we have talked a lot about developing reasoned conclusions and how to communicate recommendations based on those conclusions. Our peers in the workplace will expect us to provide good reasons for what we say. And they will expect us to clearly articulate those reasons. But there is a third aspect to developing reasoned conclusions that we have not yet talked about. Our conclusions and recommendations — particularly our recommendations — must past the ethical test. Just because something is legal —or logical — it doesn’t follow that it is ethical.
  852.  
  853. Slide 3: Ethics in the Workplace
  854. It is an old cliché that one ought not to talk about religion or politics while in polite company. The reason being, these topics are incredibly personal and so potentially divisive that they more often than not develop into heated disputes. And this rarely serves to move the debate forward, and leaves everyone involved with a bad taste in their mouth.
  855.  
  856. Ethics is included in the do-not-talk-about category, usually because of its perceived close association with religion. But this is an unfortunate misconception, as ethics and religion are two different things. Ethics has an important place in decision making in the workplace. If our actions affect the well-being of others, ethics has something to say about it. We need to talk about ethics in the workplace, but we have to be smart about it. If we aren’t, things can end badly. If you haven’t already, I encourage you to watch the Ringwearer video of Peter Ruttan.
  857.  
  858. Slide 4: Ethics in the Workplace
  859. The moral of Mr. Ruttan’s story is this: ethical disputes will occur in the workplace. Everyone has a conception of what it means to live a good life. Everyone has an opinion about what makes an action right or wrong. And everyone has a different opinion about what our moral focus should be on. What does this mean? Well, think of it this way. When you criticize someone for doing something wrong, what are you focused on? Or if you are praising someone for doing something good, what are you focused on? The answer you give depends on what ethical theory you subscribe to. There are many ethical theories, and each theory will focus on a different aspect of the good life.
  860.  
  861. Slide 5: Moral Focus
  862. Virtue theory, for example, focuses on the character of the person: is the person acting from a virtuous character? What is virtue? Aristotle said it was the middle ground between two extremes: the extreme of excess and the extreme of deficiency. Courage, for example, is a virtue. It lies between the extreme of excess (recklessness) and the extreme of deficiency (cowardliness). Deontological theories focus on moral duty: is the person doing the right thing for the right reasons? Immanuel Kant, for example, said our duty is to treat people as an end and not only as a means. In other words, our duty is to respect the moral autonomy of other humans. Moral Rights theories focus on claims and privileges that people have: is the person respecting the rights and freedoms of other people? In Canada our rights are enshrined in the Charter of Rights and Freedoms. Utilitarianism focuses on the consequences of an action: is the person acting in such a way so as to produce the greatest happiness for the greatest number of people? Finally, egoism focuses on self-interest: is the person acting out of self-interest.
  863.  
  864. Slide 6: Ethics in the Workplace
  865. This does not exhaust all of the moral theories out there. But remember the underlying point: each one of us will have a different answer to the question: What should our ethical focus be on? Our differences of opinions will inform the decisions we make in the workplace. And this can lead to tension — as we saw in the ringwearer video. Sometimes, it is appropriate to be unwavering in your values and principles; other times it is appropriate to set your personal values aside, compromise, and seek a middle ground. It is important to know how to navigate difficult ethical issues you will face in the workplace. Now consider the following thought experiment.
  866.  
  867. Slide 7: Ethical Decisions
  868. You’re out for a walk, and are about to cross a trolley track. Before you cross, you look both ways. As you do, you notice two things. First, you notice an out of control trolley coming down the track. Second, you notice that five people have been tied to the track, and if nothing is done to stop the trolley, they will be killed. You then spot a portly man standing near the tracks. You do a quick calculation in your head: If I push the heavy man onto the tracks, that might be enough to stop the trolley, thereby saving the 5 people tied to the tracks.
  869.  
  870. Slide 8: Ethical Decisions
  871. Now if you were simply considering outcomes, you might think this way: sacrificing the one for the many is the preferable choice. It is better for one person to die than for five people to die. There will be less suffering in the world if just the one dies. If you are really committed to the consequentialist way of thinking, you may even want to know what the social standing of the people are. But if you are concerned about moral rights, you might think this way: There is a moral difference between allowing harm to be done and actively harming another person. Pushing the heavy man on to the track amounts to murder, and this is unjustifiable. No one person can without consent be sacrificed for the many. Their interests must be taken into consideration. What we ultimately decide to do will be determined, in part, by our ethical convictions, our principles, and our values. And these are different for each person. And this can lead to strained relations, especially if you are working in a group and are trying to achieve group consensus.
  872.  
  873. Slide 9: Ethical Decisions
  874. Now consider a less drastic (and much more plausible) thought experiment. You and two of your friends formed a start-up company that does 3D printing. Your company is struggling. Part of the problem is with the prices charged by your main supplier, Arm-and-a-Leg Suppliers. Their prices are very high, leaving you with a very small margin for profit. So you decide to look for cheaper suppliers. You quickly find two candidates, both of which are cheaper than your current supplier: Bedraggled Printing Solutions and Capitalization Resources. However, after doing your due diligence, you learn that Bedraggled Printing solutions has a horrible environmental record. In fact, they are being currently investigated by the RCMP for dumping illegal waste. You also learn that Capitalization Resources has a history of contracting their work to subsidiaries that exploit workers in developing economies.
  875.  
  876. Slide 10: Ethical Decisions
  877. You report your findings back to your two friends and the three of you decide to take a vote on what you should do: Should you stay with the company that is costing you an arm and a leg? Should you go with the company that has a poor environmental track record? Or should you go with the company that contracts its work out to companies who exploit their workers? The three of you decide to rank your personal preferences to see which company comes out on top with the most votes.
  878.  
  879. Slide 11: Tough Decisions
  880. For the sake of argument, let’s say you voted for your current supplier. Even though the company you are currently with costs more, you think it is better to stay with the status quo than to go with a company that compromises your core values. Barring that, you think it is better to go with the company that has a poor environmental record rather than with the business that subcontracts out to companies that exploit their workers. So how did your friends vote? Well one of your friends voted for Bedraggled Printing Solutions over Capitalization Resources. They ranked your current supplier last. Clearly they are interested in cutting costs. Your other friend ranked Bedraggled Printing Solutions last. They are very concerned about the environment, so their vote is quite predictable. But they also ranked Capitalization Resources over your current supplier. Clearly they are also interested in saving money as well.
  881.  
  882. Slide 12: Tough Decisions
  883. So what’s the point in all of this? Notice the outcome of the vote. All three companies received two votes in favour and one vote against. Not only is there no clear winner, there can be no consensus among the three of you. Collectively you have an intransitive ordering of preferences. Collectively you prefer your current supplier to Bedraggled Printing Solutions; you prefer Bedraggled Printing Solutions to Capitalization Resources; and you prefer Capitalization Resources to your current supplier. If there is to be any way forward, someone’s vote will have to be dismissed. Which means that someone’s interests and underlying values will be overridden. The question is, whose?
  884.  
  885. Slide 13: Ethics in the Workplace
  886. The point is, there will be times in the workplace when decisions are made that go against your personal values; decisions that go against everything you stand for. At times it is important to stand up for what you believe — we will see a story about this in Unit 10. Other times it is important to set your personal convictions aside, compromise, and seek a middle ground.
  887.  
  888. Slide 14: Personal vs. Public Morality
  889. It is here that I want to distinguish between personal and public morality. Personal morality is the set of principles and values that you live by in your personal life. For example, these values and principles guide you as you interact with other people; they guide how treat your friends and family; and they inform the decisions you make about where to shop and do your personal business. Public morality, in contrast, is the set of moral principles and values that you act in accordance with when you act on someone’s — or something’s — behalf.
  890.  
  891. Slide 15: Personal vs. Public Morality
  892. Here’s an example. Let’s say Bob is the CEO of a start-up that writes 3D imaging software. Though his idea is not new to the market, he is optimistic that his product is different enough for his market share to grow. If he is correct, he stands to make a lot of money for himself and for his investors. However, his company is struggling at the moment and needs more capital to keep operating. He has enough money for the next two weeks, but after that things look grim and he will not be able to make payroll. Unfortunately, Bob has not been able to attract any more investors. If he doesn’t find capital soon, he, his employees, and his investors will lose everything.
  893.  
  894. Then, one day, Bob receives a phone call. The person on the other end of the line is very interested in investing in his business. The person represents one of the largest companies in the adult entertainment industry and has a lot of cash to invest in Bob’s company. This is a good thing for Bob, right? Well, the thing is, Bob is deeply religious and objects to what the adult entertainment industry stands for. Bob believes pornography is immoral and that the porn industry exploits women. The question is, what should Bob do? Should he accept the investment, or should he stick by his personal convictions and reject the money?
  895.  
  896. Slide 16: Personal vs. Public Morality
  897. Let’s look at what the Canadian law says. Section 122 of The Canada Business Corporations Act states: 122. (1) Every director and officer of a corporation in exercising their powers and discharging their duties shall (a) act honestly and in good faith with a view to the best interests of the corporation; and (b) exercise the care, diligence and skill that a reasonably prudent person would exercise in comparable circumstances.
  898.  
  899. Slide 17: Personal vs. Public Morality
  900. In other words, when faced with two options option A and option B, Bob must choose the option which best serves the company and its investors. If all information available suggests option A will grow the company faster than option B, everything else being equal, Bob must choose option A, or face the consequences. His investors have the legal right to hold Bob accountable for the decisions he makes regarding his business. In other words, Bob has a fiduciary duty to his employees and investors and must act in their best interests. Bob must set his deeply held convictions aside if he wants to carry on as CEO of his company.
  901.  
  902. Slide 18: Navigating Our Differences
  903. Ethical and moral debates are inevitable, even at work. These debates can be about small things, like whether it’s OK to buy coffee from Starbucks, to big things like whether it’s OK to do business with the Defence Department. Here’s my suggestion of how to navigate these different types of disputes. The first thing you need to do is ask yourself: What do I believe and why do I believe it? What assumptions am I making about where our moral knowledge comes from? What moral theory am I subscribing to? And once you’ve figured that out, ask the same questions about your interlocutor: What does my interlocutor believe? Why did they believe it? What assumptions are they making about where our moral knowledge comes from, and what moral theory do they subscribe to? Figuring this out is an important first step. Progress can’t be made if you don’t understand where either you or your partner is coming from.
  904.  
  905. Once you’ve established your respective starting points, you can then ask what the core issues are. Clarity on this point is crucial. You’ll never come to a rational resolution if you don’t know what the core issues are. And if you can’t pin down what the core issue is, look to see if the goalposts are moving. If you’re playing a game of soccer and the opposing team keeps moving the goalposts, it’ll be virtually impossible for you to score a goal. By analogy, if you’re in argument with someone but the issues keep changing, you’re just arguing for the sake of arguing at that point, and no rational solution can emerge. So be sure you understand what the core issues are. Once you’ve determined what your respective starting points are, and once you’ve established what the main issues are, look for a compromise. You might not win the debate, and your interlocutor might not win the debate, but winning isn’t the point.
  906.  
  907. The point is to come to a compromise that everyone can live with and one that will allow progress to be made. Don’t make it a clash of the wills. No one wins at that point. If you make it a clash of the wills, you’ve made the debate about you, not the principles you purportedly stand for. You’ve moved the goalposts. And if it’s beginning to look like a compromise is not possible, then ask yourself, what are the consequences if we can’t come to a compromise? Everything we do comes at a cost of some sort, whether they be economic, personal, social, or whatever. And if the costs of not coming to a compromise outweigh the costs of coming to a compromise, then you better find a compromise. Don’t raise the stakes of the debate unless you’re willing to pay the cost of doing so. Again, keep your ego out of it. Don’t make the debate about you. This can be a difficult thing, especially when it comes to moral and ethical debates.
  908.  
  909. Slide 19: Conclusion
  910. The question of how we should live our lives and behave towards others is not just an academic question for philosophers. Each one of us has answers to the question, how do we know what the right thing to do is? And each of us has an answer to the question, what should the focus of our moral deliberations be? Each one of us has answers because each one of us has a stake in the debate. Our answers to these questions affect our decisions at work and how we interact with our co-workers. And although the moral and ethical debates in the workplace can be tricky to navigate, the cost of walking away with our fingers in our ears is too high. But with a good dose of patience, tolerance, and open- mindedness, compromise and progress is possible.
  911.  
  912.  
  913.  
  914. © University of Waterloo
  915.  
  916.  
  917. Transcript: Great Expectations
  918. Slide 1: Great Expectations
  919. [introductory music]
  920.  
  921. Slide 2: Great Expectations
  922. Everyone has a conception of what it means to live a good life. Everyone has an opinion about what makes an action right or wrong. And everyone has an opinion about what our moral focus should be on. And these opinions will inform the decisions we make in the workplace. It is almost inevitable that ethical disputes will occur.
  923.  
  924. Slide 3: Personal vs. Public Morality
  925. In Unit 9, I distinguished between personal and public morality. Your personal morality represents the values and principles by which you live your life as a private citizen. In contrast, public morality represents the values and principles you must adhere to while working on behalf of your organization or company. And as I mentioned in Unit 9, there are times when it is entirely appropriate to be unwavering in your personal values. But there are also times when it is wise to set your personal convictions and values aside, compromise, and seek a middle ground.
  926.  
  927. Slide 4: Great Expectations
  928. Compromise can be difficult, since our values and beliefs about what is right or wrong make up an important part of who we are. So when we do compromise our personal values, it can feel as though we have sold ourselves out. Conversely, if we stick to our principles, we feel like we are being true to ourselves. It shows that we are a person of principle. We’ve all heard the expression, it’s the principle of the matter! We like to think of ourselves as people of principle.
  929.  
  930. Slide 5: Great Expectations
  931. But there are opportunity costs to being unwavering in our principles. If we are unwilling to compromise, we treat ethical disputes as zero-sum. Here is the basic idea to zero-sum thinking: what I win comes at a cost to you; and what you win comes at a cost to me. Only one person can win. Poker is an excellent example of a zero-sum game: the only way a person wins is if everyone else at the table loses.
  932.  
  933. Slide 6: Great Expectations
  934. Zero-sum thinking can be incredibly destructive in the workplace, particularly when it comes to ethical disagreements. Consider the underlying logic of zero-sum thinking in this particular instance: Premise 1: I believe that I am right. Premise 2: I wouldn’t knowingly believe anything that is wrong. Premise 3: You disagree with me. Conclusion: You are wrong. It is easy to see why this form of reasoning is so appealing. However, it leaves absolutely no room for seeking a middle ground. And when we fall into this way of thinking, we miss the opportunity to see things from a different, and more often than not, legitimate, point of view.
  935.  
  936. Slide 7: Great Expectations
  937. So what should we do? We should start where there is common ground. We start by looking to codes of ethics, and we do so on two levels: at the level of the profession, and at the level of the company.
  938.  
  939. Slide 8: Code of Ethics
  940. I introduced you to the Professional Engineers code of ethics in Unit 1. It is worth revisiting what Section 77 of Ontario’s Professional Engineers Act says: As an engineer, it is expected that you will act out of fairness and loyalty to your employers and fellow employees; that you will be faithful to the needs of the public; that you will act with integrity; that you will stay abreast of developments in your field; and that you will perform your duties with competence.
  941.  
  942. Slide 9: Code of Ethics
  943. Further, you will be held accountable for what you say. It is expected that you will discourage untrue, unfair or exaggerated claims. In other words, that you will be truthful in what you say. It is expected that you will know what you are talking about, and that you will base your claims on adequate knowledge and honest conviction. When ethical disagreements arise in the workplace, disagreements about what should or should not be done — your profession’s Code of Ethics is a good place to find common ground. It is an objective measure that is — in many cases — formalized in law. A similar thing can be said of your company’s code of ethics. If you are unsure of what should be done — from an ethical point of view — take a look at your company’s code of ethics. Take that as your starting point. Now when you look at the PEO Code of Ethics or your company’s code of ethics, you may think to yourself: This isn’t very specific. It doesn’t tell me what I should or shouldn’t do. It is important to distinguish between a code of ethics and a code of conduct.
  944.  
  945. Slide 10: Code of Ethics vs. Code of Conduct
  946. A code of conduct is very specific. It is, in essence, a list of dos and don’ts. It is a list of behaviours that are either prescribed — something that you must do, or proscribed — something you are forbidden from doing. A code of conduct is enforceable, and each company will have a punishment mechanism in place to deal with employees who are non-compliant. A code of conduct is useful in that it ensures each employee knows what is expected of them, and ensures each employee knows — roughly — how to behave in the workplace. In contrast, a code of ethics is a general statement of values and principles the company (or profession) aspires to. It is not a list of rules. Think of it as a statement of ambition. Not every engineer will act competently in every situation. But that doesn’t mean we can’t — or shouldn’t — aspire to act competently in every situation. A code of ethics is not a list of dos and don’ts. But it does provide guidance for employees. It is a way for a company to establish its corporate culture. There are two benefits to having a code of ethics. First, it builds trust amongst shareholders and stakeholders alike. But for trust to be built, stakeholders must see that company takes the code of ethics seriously. It is not enough to simply have a code of ethics. It must become a part of the company’s culture: employees must buy into it; managers must make decisions by it; and executives must not behave like they are above and beyond it. Otherwise, stakeholders will see the company’s code of ethics simply as a cynical ploy to manipulate public opinion. The second benefit of having a code of ethics is that it empowers employees to make decisions. Anyone can mindlessly follow a set of rules. But making decisions based on values and principles requires judgment, maturity, and an astute ability to develop reasoned conclusions. Remember, a code of ethics is general. Hence, it is open to interpretation and requires judgment to act on. Here are two examples to illustrate the point.
  947.  
  948. Slide 11: Code of Conduct
  949. In 2014, a 9-year old girl in Grand Junction, Colorado decided to shave her head. Why? Because her close friend was fighting cancer and had lost all of her hair because of chemotherapy. Shaving her head was an act of solidarity. Her school, however, told her she could not come to school with a shaved head. If she wanted to attend school, she would have to wear a wig. The reason they gave was because a shaved head violated the school’s dress code, a section of their code of conduct. Quoting the schools administrator, Fox news writes: The dress code was created to promote safety, uniformity, and a non-distracting environment for the school’s students. Catherine Norton Breman, president and chair of the academy’s board of directors, said in a statement, under this policy, shaved heads are not permitted.
  950.  
  951. Slide 12: Code of Conduct
  952. A code of conduct is a set of rules. If the rules are not followed, punishment must be meted out. The administrators’ hands were tied. They had no choice but to enforce the rules. A code of conduct does not allow for judgment calls to be made.
  953.  
  954. Slide 13: Ethical Decisions
  955. Now contrast this with the story of Evan Vokes, an engineer and former employee of TransCanada Pipeline. While working for TransCanada Pipeline, Evan Vokes witnessed many regulatory infractions. CBC reports: Evan Vokes said he raised concerns about the competency of some pipeline inspectors and the company’s lack of compliance with welding regulations set by the National Energy Board (NEB), the federal energy industry regulator. The article continues: Many of the complaints by Vokes focused on TransCanada’s practice of allowing its pipeline and fabrication contractors to hire the inspectors that would be inspecting their contractors’ work.
  956.  
  957. Slide 14: Ethical Decisions
  958. Regulations required TransCanada to hire independent welding inspectors. But this was not happening. Instead, TransCanada Pipeline allowed their subcontractors to hire their own inspectors. This is a perverse incentive. If you are an inspector and you are too critical of the people who pay you, odds are you are not going to keep your job for too long. Vokes saw this a risk to both the public and the environment. If subpar welding was being passed, this only increased the chances of something bad — even catastrophic — happening in the future.
  959.  
  960. Slide 15: Ethical Decisions
  961. Why did Vokes feel the need to do something about what he saw? Consider what section 4.1 of The Association of Professional Engineers and Geoscientists of Alberta states: Professional engineers and geoscientists shall, in their area of practice, hold paramount the health, safety and welfare of the public, and have regard for the environment. But notice Section 4.1 says nothing about welding, best practices in hiring inspectors, or regulatory compliance. Yet Section 4.1 clearly applied to the situation in which Mr. Vokes found himself. What is stated in Section 4.1 is a general principle (not a rule). And Mr. Vokes took it upon himself — as a professional — to act in accordance with the principle. Why did Mr. Vokes act the way he did? Because he knew that that was what was expected of him as a professional engineer. It was not easy, and it took a lot of courage. But he did what was expected of him.
  962.  
  963. Slide 16: Conclusion
  964. Don’t get me wrong. There are times when compromise is necessary. But there are also times when you need to stand up for what is right. The story of Mr Vokes is one of integrity and courage. Use your profession’s code of ethics to guide you. Use your company’s code of ethics to guide you. That is what they are there for. Even if you are not in the midst of a dilemma right now, it is a good idea to at least familiarize yourself with your profession’s code of ethics. Remember that ignorance is not an excuse. Your colleagues will expect much more from you.
  965.  
  966.  
  967.  
  968. © University of Waterloo
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