Why p-values dont mean what you think they mean

P-values are the most overused and over-interpreted statistic.Explanations are provided for why, typically, p-values don't mean what scientists think the mean. Toward the end an app is used to further illustrate the points.

R code to run app: install.packages("shiny") shiny::runGitHub("jarad/pvalues")

ASA statement on p-values: https://amstat.tandfonline.com/doi/full/10.1080/00031305.2016.1154108

R code: shiny::runGitHub('jarad/pvalue') App: https://jaradniemi.shinyapps.io/pvalue/

Probability playlist: https://www.youtube.com/playlist?list=PLFHD4aOUZFp1FxJs9BG5Sbsy6NvCO3Qb1 Statistics playlist: https://www.youtube.com/playlist?list=PLFHD4aOUZFp1PZC6SgtuS-ESq4ti1GEFj

STAT 587: https://www.jarad.me/courses/stat587Eng/ STAT 587 Videos: https://www.jarad.me/courses/stat587Eng/slides/ Slides: https://www.jarad.me/courses/stat587Eng/slides/Inference/I06-Pvalues/why_pvalues_dont_mean_what_you_think_they_mean.pdf

00:20 - p-value definition 01:29 - t-test 02:43 - interpreting p-values 05:47 - context matters 10:20 - a simple model 14:10 - links to the shiny app 14:55 - app demonstration 21:38 - summary

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