Why p-values are not as useful as we might think

A repository of links to papers that articulate why p-values don't tell us what most people think they tell us.

Sorry Sir Ronald Fisher, I love you, but I don't love your p-values.

Quotes taken from the links below just in case you like it spicy:

The most important task before us in developing statistical science is to demolish the P-value culture, which has taken root to a frightening extent in many areas of both pure and applied science, and technology. (Nelder, 1999, p. 261)

My personal view is that p-values should be relegated to the scrap heap and not considered by those who wish to think and act coherently. (Lindley, 1999, p. 75)

"It is extraordinarily difficult to find a statistician who argues explicitly in favor of the retention of significance tests ..." (Oakes, 1986)

The implication of hypothesis testing- that there can always be a simple "yes" or "no" answer as the fundamental result from a medical study-is clearly false and used in this way hypothesis testing is of limited value. (Gardner, 1986)

Arguments against p-values

1. Why P Values Are Not a Useful Measure of Evidence in Statistical Significance Testing - https://journals.sagepub.com/doi/pdf/10.1177/0959354307086923
2. P values are only an index to evidence: 20th- vs. 21st-century statistical science - https://www.jstor.org/stable/pdf/43495188.pdf
3. Statistical reform in medicine, psychology and ecology - https://www.sciencedirect.com/science/article/pii/S1053535704000940
4. Confidence intervals rather than P values: estimation rather than hypothesis testing - https://www.bmj.com/content/bmj/292/6522/746.full.pdf

Why p-values are alright

1. feel free to make a pull request to add stuff here.