# Modelling publication bias and p-hacking

arXiv: https://arxiv.org/abs/1911.12445 · HTML: https://mlsanity.com/paper/1911.12445/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [Strategic Hypothesis Testing](https://mlsanity.com/paper/2508.03289/) (2025)

## Abstract (arXiv)

Publication bias and p-hacking are two well-known phenomena that strongly affect the scientific literature and cause severe problems in meta-analyses. Due to these phenomena, the assumptions of meta-analyses are seriously violated and the results of the studies cannot be trusted. While publication bias is almost perfectly captured by the weighting function selection model, p-hacking is much harder to model and no definitive solution has been found yet. In this paper we propose to model both publication bias and p-hacking with selection models. We derive some properties for these models, and we compare them formally and through simulations. Finally, two real data examples are used to show how the models work in practice.