[17:51] <LaptopGW> Tark, pitch me for the second categories?
[17:52] <LaptopGW> I'm not entirely convinced either way
[17:52] <LaptopGW> Convince me?
[18:22] <Tark> so here you go ghost
[18:23] <Tark> which of the following is more useful: 1) this article has an average rating of 4 (with 2 users rating); 2) this article was well generally liked by our raters
[18:25] <Tark> or 3) this article has a 75%
[18:25] <LaptopGW> I think either 1 or 3
[18:25] <LaptopGW> Maybe 3, since it makes a distinction between 61% and 80%
[18:26] <Cid> OK, I LoL now
[18:26] <Tark> well, the problem with 3 is that you can't actually hit 74% or 76%
[18:26] <LaptopGW> Even with enough users? O.o
[18:26] <LaptopGW> (Ratings)
[18:26] <Tark> enough is ~40
[18:26] <LaptopGW> I realize that's impossible with only 2-3 ratings... but with ~10?
[18:26] <LaptopGW> Between 3s, 4s, and 5s?
[18:26] <Tark> yeah
[18:26] <Tark> do the math, i'll wait
[18:26] <Tark> or rather come back
[18:26] <Tark> also, 75% of what?
[18:26] <LaptopGW> Remember that 1 = 0%, 2 = 25%, 3 = 50%, 4 = 75%, and 5 = 100%
[18:27] <Tark> yeah
[18:27] <Tark> i know
[18:27] <Tark> with 10 ratings you start to get within 2-3%
[18:28] <Tark> it's way more granular than we have any expectation of ever using
[18:28] <LaptopGW> *noddish* While I can see your point, I'm not sure that's a really strong argument to not using it
[18:28] <Tark> and it relies on a winning vote in the other section to matter even that much
[18:28] <Tark> would you use a d100 instead of a d20 in dnd?
[18:28] <LaptopGW> Heck no
[18:29] <Tark> the same arguments apply
[18:29] <LaptopGW> Unless you had enough stacking things to matter (if you do, use d100 and make them +1s and +2s), then d20 is so much simpler to calculate at the table in real time
[18:29] <LaptopGW> But I'm not sure that applies here, does it?
[18:29] <Tark> if you're not going to use the granularity, don't allow for it
[18:29] <Tark> the alternative is choice 1
[18:29] <Tark> which i hate much less than %s
[18:30] <Tark> because it maps better to the ratings granularity