ordinal t-test vs wilcoxon.R

set.seed(1)

# genarate random paired sets and get p-values for t-test and wolcoxon test
ps = replicate(10000, (function() {
  
  temp = tibble(
    subject = paste0("S", 1:100),
    before = rnorm(100, 5, 1) %>% round() %>% pmin(7) %>% pmax(1)
  ) %>% mutate(after = (before + rnorm(100,0,1)) %>% round() %>% pmin(7) %>% pmax(1))
  
  tibble(
    p_t = t.test(temp$before, temp$after, paired = TRUE)$p.value,
    p_w = wilcox.test(temp$before, temp$after, paired = TRUE)$p.value
)})(), simplify = FALSE) %>% bind_rows() 

# what percent of p-values are below 0.05
ps %>% 
  summarise(
    p_t = mean(p_t<0.05) %>% scales::label_percent(.1)(),
    p_w = mean(p_w<0.05) %>% scales::label_percent(.1)(),
  )