#Bootstrap:
n <- length(data)
beta <- 400
results <- rep(NA, beta)
for(i in 1:beta){
	sample(data, n, replacement=T) %>% mean -> results[i]
}
mean(results)
sd(results)

#Bootstrap 2:
n <- length(data)
beta <- 400
mean_fxn <- function(data, index){
	mean(data[index])
}
boot(mean_fxn, beta, data)

#Randomization:
obs_dif <- mean(a) - mean(b)
len_a <- length(a)
ab <- c(a, b)
beta <- 400
means <- rep(NA, beta)
for(i in 1:beta){
	samp <- sample(ab)
	a2 <- mean(samp[1:len_a])
	b2 <- mean(samp[-(1:len_a)])
	means[i] <- a2 - b2
}
if(obs_dif > 0){
	num_extreme <- means > obs_dif
} else {
	num_extreme <- means < obs_dif
}
count(num_extreme)/beta