# Strategic Hypothesis Testing

arXiv: https://arxiv.org/abs/2508.03289 · HTML: https://mlsanity.com/paper/2508.03289/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [Strategic Classification](https://mlsanity.com/paper/1506.06980/) (2015)
- [Strategic Classification from Revealed Preferences](https://mlsanity.com/paper/1710.07887/) (2017)
- [Strategyproof Linear Regression in High Dimensions](https://mlsanity.com/paper/1805.10693/) (2018)
- [Linear Classifiers that Encourage Constructive Adaptation](https://mlsanity.com/paper/2011.00355/) (2020)
- [Strategic Apple Tasting](https://mlsanity.com/paper/2306.06250/) (2023)
- [Incentive-Theoretic Bayesian Inference for Collaborative Science](https://mlsanity.com/paper/2307.03748/) (2023)
- [Sharp Results for Hypothesis Testing with Risk-Sensitive Agents](https://mlsanity.com/paper/2412.16452/) (2024)
- [The Effect of Strategic Noise in Linear Regression](https://mlsanity.com/paper/2007.07316/) (2020)
- [Modelling publication bias and p-hacking](https://mlsanity.com/paper/1911.12445/) (2019)

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

- [Incentive Aware AI Regulations: A Credal Characterisation](https://mlsanity.com/paper/2603.05175/) (2026)

## Abstract (arXiv)

We examine hypothesis testing within a principal-agent framework, where a strategic agent, holding private beliefs about the effectiveness of a product, submits data to a principal who decides on approval. The principal employs a hypothesis testing rule, aiming to pick a p-value threshold that balances false positives and false negatives while anticipating the agent's incentive to maximize expected profitability. Building on prior work, we develop a game-theoretic model that captures how the agent's participation and reporting behavior respond to the principal's statistical decision rule. Despite the complexity of the interaction, we show that the principal's errors exhibit clear monotonic behavior when segmented by an efficiently computable critical p-value threshold, leading to an interpretable characterization of their optimal p-value threshold. We empirically validate our model and these insights using publicly available data on drug approvals. Overall, our work offers a comprehensive perspective on strategic interactions within the hypothesis testing framework, providing technical and regulatory insights.