# 19.3: Simulating Statistical Power

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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.

This page titled 19.3: Simulating Statistical Power 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.