P-value bowling
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I recently went bowling, and you know those weird 3D-animated bowling animations that all bowling alleys seemed to show whenever you made a strike? They are still alive and well! (At least at my local bowling place). And then I thought: Can I get animations like that into my daily data science workflow? With
Rstudio’s built-in Viewer tab, I absolutely could! Below you find the code for a much improved t.test function that gives you different animations when you hit a strike ($p < 0.01$), a spare ($p < 0.05$), a “near miss” ($p < 0.1$) and a complete miss ($p > 0.1$).
(If you think this is silly, then I agree. Roughly as silly as using ritualized p-value cutoffs to decide whether an experiment is a “success” or not.)
The bowling.t.test function
# The animations we're going to use
# Source: https://archive.org/details/brunswick-frameworx-ten-pin-animations/
bowling_animation_urls <- c(
"0.01" = "https://i.imgur.com/Kn8CQbj.gif",
"0.05" = "https://i.imgur.com/HFTKdDL.gif",
"0.1" = "https://i.imgur.com/8Sw54Mz.gif",
"1" = "https://i.imgur.com/kjROdhj.gif"
)
bowling_animation_html <- sapply(bowling_animation_urls, function(url) {
# We need to download the animations to the R session's temp directory
# to display them in the Rstudio Viewer pane
animation_path = tempfile(pattern = "bowling_animation", fileext = ".gif")
download.file(url, animation_path)
# And we need to create an HTML wrapper around each GIF
html_path = tempfile(pattern = "bowling_animation", fileext = ".html")
html <- paste0(
'<html><body><img src="',
basename(animation_path) ,
'" style="width: 100%;height: auto;"/></body></html>'
)
writeLines(html, html_path)
html_path
})
# A much improved t.test function
bowling.t.test <- function(...) {
test_result <- t.test(...)
selected_animation_html <- bowling_animation_html[
test_result$p.value < as.numeric(names(bowling_animation_urls))
][1]
rstudioapi::viewer(selected_animation_html)
test_result
}
And now trying it out!
set.seed(123) y1 = rnorm(30, mean = 2) y2 = rnorm(30) # A successful experiment, we're great scientists. bowling.t.test(y1, y2) set.seed(123) y1 = rnorm(30, mean = 0.7) y2 = rnorm(30) # Oh, that was close, but we're still great scientists. bowling.t.test(y1, y2) set.seed(123) y1 = rnorm(30, mean = 0.65) y2 = rnorm(30) # What bad luck! Let's say it's trending towards significance maybe? bowling.t.test(y1, y2) set.seed(123) y1 = rnorm(30, mean = 0.5) y2 = rnorm(30) # What are we even doing with our lives... bowling.t.test(y1, y2)
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