# 19.3: Simulating Statistical Power

- Page ID
- 8816

Let’s simulate this to see whether the power analysis actually gives the right answer. We will sample data for two groups, with a difference of 0.5 standard deviations between their underlying distributions, and we will look at how often we reject the null hypothesis.

```
nRuns <- 5000
effectSize <- 0.5
# perform power analysis to get sample size
pwr.result <- pwr.t.test(d=effectSize, power=.8)
# round up from estimated sample size
sampleSize <- ceiling(pwr.result$n)
# create a function that will generate samples and test for
# a difference between groups using a two-sample t-test
get_t_result <- function(sampleSize, effectSize){
# take sample for the first group from N(0, 1)
group1 <- rnorm(sampleSize)
group2 <- rnorm(sampleSize, mean=effectSize)
ttest.result <- t.test(group1, group2)
return(tibble(pvalue=ttest.result$p.value))
}
index_df <- tibble(id=seq(nRuns)) %>%
group_by(id)
power_sim_results <- index_df %>%
do(get_t_result(sampleSize, effectSize))
p_reject <-
power_sim_results %>%
ungroup() %>%
summarize(pvalue = mean(pvalue<.05)) %>%
pull()
p_reject
```

`## [1] 0.8`

This should return a number very close to 0.8.