A taxonomy of model assumptions on which P is based and implications for added benefit in the sciences

ABSTRACT Although the null hypothesis significance testing procedure is problematic, many still favor the use of p-values as indicating the state of evidence against the model used to generate the p-value. From this perspective, p-values benefit science; or would benefit science if used correctly. In contrast, the novel argument to be presented introduces a taxonomy of assumptions included in the model; such as theoretical assumptions, auxiliary assumptions, statistical assumptions, and inferential assumptions. Careful attention is paid to the different categories of assumptions that models necessarily include to render p-value calculations sensible. Such careful attention suggests unappreciated limitations of p-values. Considering these limitations in the context of the descriptive statistics researchers routinely have available to them clarifies that p-values provide no added benefit to the scientist, above and beyond such descriptive statistics. The lack of added value, combined with the obvious harms documented in recent reviews, suggests that researchers in the sciences should rarely, or never, use p-values. Not even for indicating the state of evidence against models.