# Chapter 17 Hypothesis testing in R

In this chapter we will present several examples of using R to perform hypothesis testing.

## 17.1 Simple example: Coin-flipping (Section 16.3.5.1)

Let’s say that we flipped 100 coins and observed 70 heads. We would like to use these data to test the hypothesis that the true probability is 0.5.

First let’s generate our data, simulating 100,000 sets of 100 flips. We use such a large number because it turns out that it’s very rare to get 70 heads, so we need many attempts in order to get a reliable estimate of these probabilties. This will take a couple of minutes to complete.

# simulate tossing of 100,000 flips of 100 coins to identify
# empirical probability of 70 or more heads out of 100 flips

nRuns <- 100000

# create function to toss coins
tossCoins <- function() {
flips <- runif(100) > 0.5
}

# create an input data frame for do()
input_df <- tibble(id=seq(nRuns)) %>%
group_by(id)

# use do() to perform the coin flips
flip_results <- input_df %>%
do(tossCoins()) %>%
ungroup()

p_ge_70_sim <-
flip_results %>%
summarise(p_gt_70 = mean(nHeads >= 70)) %>%
pull()

p_ge_70_sim
##  3e-05

For comparison, we can also compute the p-value for 70 or more heads based on a null hypothesis of $$P_{heads}=0.5$$, using the binomial distribution.

# compute the probability of 69 or fewer heads,
p_lt_70 <- pbinom(69, 100, 0.5)

# the probability of 70 or more heads is simply
# the complement of p_lt_70
p_ge_70 <- 1 - p_lt_70

p_ge_70
##  3.9e-05

## 17.2 Simulating p-values

In this exercise we will perform hypothesis testing many times in order to test whether the p-values provided by our statistical test are valid. We will sample data from a normal distribution with a mean of zero, and for each sample perform a t-test to determine whether the mean is different from zero. We will then count how often we reject the null hypothesis; since we know that the true mean is zero, these are by definition Type I errors.

nRuns <- 5000

# create input data frame for do()
input_df <- tibble(id=seq(nRuns)) %>%
group_by(id)

# create a function that will take a sample
# and perform a one-sample t-test

sample_ttest <- function(sampSize=32){
tt.result <- t.test(rnorm(sampSize))
return(tibble(pvalue=tt.result\$p.value))
}

# perform simulations

sample_ttest_result <- input_df %>%
do(sample_ttest())

p_error <-
sample_ttest_result %>%
ungroup() %>%
summarize(p_error = mean(pvalue<.05)) %>%
pull()

p_error
##  0.048

We should see that the proportion of samples with $$p < .05$$ is about 5%.