# 13.2: Central Limit Theorem

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The central limit theorem tells us that the sampling distribution of the mean becomes normal as the sample size grows. Let’s test this by sampling a clearly non-normal variable and look at the normality of the results using a Q-Q plot. We saw in Figure @ref{fig:alcDist50} that the variable AlcoholYear is distributed in a very non-normal way. Let’s first look at the Q-Q plot for these data, to see what it looks like. We will use the stat_qq() function from ggplot2 to create the plot for us.

# prepare the dta
NHANES_cleanAlc <- NHANES %>%
drop_na(AlcoholYear)

ggplot(NHANES_cleanAlc, aes(sample=AlcoholYear)) +
stat_qq() +
# add the line for x=y
stat_qq_line()

We can see from this figure that the distribution is highly non-normal, as the Q-Q plot diverges substantially from the unit line.

Now let’s repeatedly sample and compute the mean, and look at the resulting Q-Q plot. We will take samples of various sizes to see the effect of sample size. We will use a function from the dplyr package called do(), which can run a large number of analyses at once.

set.seed(12345)

sampSizes <- c(16, 32, 64, 128) # size of sample
nsamps <- 1000 # number of samples we will take

# create the data frame that specifies the analyses
input_df <- tibble(sampSize=rep(sampSizes,nsamps),
id=seq(nsamps*length(sampSizes)))

# create a function that samples and returns the mean
# so that we can loop over it using replicate()
get_sample_mean <- function(sampSize){
meanAlcYear <-
NHANES_cleanAlc %>%
sample_n(sampSize) %>%
summarize(meanAlcoholYear = mean(AlcoholYear)) %>%
pull(meanAlcoholYear)
return(tibble(meanAlcYear = meanAlcYear, sampSize=sampSize))
}

# loop through sample sizes
# we group by id so that each id will be run separately by do()
all_results = input_df %>%
group_by(id) %>%
# "." refers to the data frame being passed in by do()
do(get_sample_mean(.\$sampSize))

Now let’s create separate Q-Q plots for the different sample sizes.

# create empty list to store plots

qqplots = list()

for (N in sampSizes){
sample_results <-
all_results %>%
filter(sampSize==N)

qqplots[[toString(N)]] <- ggplot(sample_results,
aes(sample=meanAlcYear)) +
stat_qq() +
# add the line for x=y
stat_qq_line(fullrange = TRUE) +
ggtitle(sprintf('N = %d', N)) +
xlim(-4, 4)

}

plot_grid(plotlist = qqplots)

This shows that the results become more normally distributed (i.e. following the straight line) as the samples get larger.

This page titled 13.2: Central Limit Theorem is shared under a not declared license and was authored, remixed, and/or curated by Russell A. Poldrack via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.