Second-generation p-values: improved rigor, reproducibility, & transparency in statistical analyses

TL;DR

The paper introduces second-generation p-values, a novel statistical method that improves scientific relevance, control of false discoveries, and transparency by incorporating effect size relevance into significance testing.

Contribution

It presents a new extension of p-values that accounts for scientific relevance and offers better control over Type I errors and multiple comparisons.

Findings

Second-generation p-values effectively distinguish between null and alternative hypotheses.

They reduce false discovery rates in data-rich environments.

The method enhances transparency and reproducibility in scientific analyses.

Abstract

Verifying that a statistically significant result is scientifically meaningful is not only good scientific practice, it is a natural way to control the Type I error rate. Here we introduce a novel extension of the p-value - a second-generation p-value - that formally accounts for scientific relevance and leverages this natural Type I Error control. The approach relies on a pre-specified interval null hypothesis that represents the collection of effect sizes that are scientifically uninteresting or are practically null. The second-generation p-value is the proportion of data-supported hypotheses that are also null hypotheses. As such, second-generation p-values indicate when the data are compatible with null hypotheses, or with alternative hypotheses, or when the data are inconclusive. Moreover, second-generation p-values provide a proper scientific adjustment for multiple comparisons and reduce false discovery rates. This is an advance for environments rich in data, where traditional p-value adjustments are needlessly punitive. Second-generation p-values promote transparency, rigor and reproducibility of scientific results by a priori specifying which candidate hypotheses are practically meaningful and by providing a more reliable statistical summary of when the data are compatible with alternative or null hypotheses.