# Abandon Statistical Significance

arXiv: https://arxiv.org/abs/1709.07588 · HTML: https://mlsanity.com/paper/1709.07588/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [Deep Reinforcement Learning at the Edge of the Statistical Precipice](https://mlsanity.com/paper/2108.13264/) (2021)
- [Do Language Models Enjoy Their Own Stories? Prompting Large Language Models for Automatic Story Evaluation](https://mlsanity.com/paper/2405.13769/) (2024)
- [Towards Inferential Reproducibility of Machine Learning Research](https://mlsanity.com/paper/2302.04054/) (2023)
- [t-Testing the Waters: Empirically Validating Assumptions for Reliable A/B-Testing](https://mlsanity.com/paper/2502.04793/) (2025)
- [On Fixing the Right Problems in Predictive Analytics: AUC Is Not the Problem](https://mlsanity.com/paper/2404.06989/) (2024)

## Abstract (arXiv)

We discuss problems the null hypothesis significance testing (NHST) paradigm poses for replication and more broadly in the biomedical and social sciences as well as how these problems remain unresolved by proposals involving modified p-value thresholds, confidence intervals, and Bayes factors. We then discuss our own proposal, which is to abandon statistical significance. We recommend dropping the NHST paradigm--and the p-value thresholds intrinsic to it--as the default statistical paradigm for research, publication, and discovery in the biomedical and social sciences. Specifically, we propose that the p-value be demoted from its threshold screening role and instead, treated continuously, be considered along with currently subordinate factors (e.g., related prior evidence, plausibility of mechanism, study design and data quality, real world costs and benefits, novelty of finding, and other factors that vary by research domain) as just one among many pieces of evidence. We have no desire to "ban" p-values or other purely statistical measures. Rather, we believe that such measures should not be thresholded and that, thresholded or not, they should not take priority over the currently subordinate factors. We also argue that it seldom makes sense to calibrate evidence as a function of p-values or other purely statistical measures. We offer recommendations for how our proposal can be implemented in the scientific publication process as well as in statistical decision making more broadly.