Central Limit Theorem in Practice

Let’s use our data to see how well the central limit theorem approximates sample averages from our data. We will leverage our entire population dataset to compare the results we obtain by actually sampling from the distribution to what the CLT predicts.

dat <- read.csv("mice_pheno.csv") #file was previously downloaded
##   Sex Diet Bodyweight
## 1   F   hf      31.94
## 2   F   hf      32.48
## 3   F   hf      22.82
## 4   F   hf      19.92
## 5   F   hf      32.22
## 6   F   hf      27.50

Start by selecting only female mice since males and females have different weights. We will select three mice from each population.

## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##     filter, lag
## The following objects are masked from 'package:base':
##     intersect, setdiff, setequal, union
controlPopulation <- filter(dat,Sex == "F" & Diet == "chow") %>%  
  select(Bodyweight) %>% unlist
hfPopulation <- filter(dat,Sex == "F" & Diet == "hf") %>%  
  select(Bodyweight) %>% unlist

We can compute the population parameters of interest using the mean function.

mu_hf <- mean(hfPopulation)
mu_control <- mean(controlPopulation)
print(mu_hf - mu_control)
## [1] 2.375517

Compute the population standard deviations as well. We do not use the R function sd because this would compute the estimates that divide by the sample size - 1 and we want the population estimates.

We can see that with R code:

x <- controlPopulation
N <- length(x)
populationvar <- mean((x-mean(x))^2)
identical(var(x), populationvar)
## [1] FALSE
identical(var(x)*(N-1)/N, populationvar)
## [1] TRUE

So to be mathematically correct, we do not use sd or var. Instead, we use the popvar and popsd function in rafalib:

sd_hf <- popsd(hfPopulation)
sd_control <- popsd(controlPopulation)

Remember that in practice we do not get to compute these population parameters. These are values we never see. In general, we want to estimate them from samples.

N <- 12
hf <- sample(hfPopulation, 12)
control <- sample(controlPopulation, 12)

As we described, the CLT tells us that for large , each of these is approximately normal with average population mean and standard error population variance divided by . We mentioned that a rule of thumb is that should be 30 or more. However, that is just a rule of thumb since the preciseness of the approximation depends on the population distribution. Here we can actually check the approximation and we do that for various values of .

Now we use sapply and replicate instead of for loops, which makes for cleaner code (we do not have to pre-allocate a vector, R takes care of this for us):

Ns <- c(3,12,25,50)
B <- 10000 #number of simulations
res <-  sapply(Ns,function(n) {

Now we can use qq-plots to see how well CLT approximations works for these. If in fact the normal distribution is a good approximation, the points should fall on a straight line when compared to normal quantiles. The more it deviates, the worse the approximation. In the title, we also show the average and SD of the observed distribution, which demonstrates how the SD decreases with as predicted.

for (i in seq(along=Ns)) {
  titleavg <- signif(mean(res[,i]),3)
  titlesd <- signif(popsd(res[,i]),3)
  title <- paste0("N=",Ns[i]," Avg=",titleavg," SD=",titlesd)

Quantile versus quantile plot of simulated differences versus theoretical normal distribution for four different sample sizes.

Here we see a pretty good fit even for 3. Why is this? Because the population itself is relatively close to normally distributed, the averages are close to normal as well (the sum of normals is also a normal). In practice, we actually calculate a ratio: we divide by the estimated standard deviation. Here is where the sample size starts to matter more.

Ns <- c(3,12,25,50)
B <- 10000 #number of simulations
##function to compute a t-stat
computetstat <- function(n) {
  y <- sample(hfPopulation,n)
  x <- sample(controlPopulation,n)
res <-  sapply(Ns,function(n) {
for (i in seq(along=Ns)) {

Quantile versus quantile plot of simulated ratios versus theoretical normal distribution for four different sample sizes.

So we see that for , the CLT does not provide a usable approximation. For , there is a slight deviation at the higher values, although the approximation appears useful. For 25 and 50, the approximation is spot on.

This simulation only proves that is large enough in this case, not in general. As mentioned above, we will not be able to perform this simulation in most situations. We only use the simulation to illustrate the concepts behind the CLT and its limitations. In future sections, we will describe the approaches we actually use in practice.