FiveThirtyEight reader responses to a food frequency questionnaire (FFQ).
Why you shouldn't peek at significance levels to decide when to stop an experiment
A Satirical-but-Theoretically-Grounded Treatment of the Only Technique You Will Ever Need in Machine Learning Research
A curated exploration of the Human Factor in Data Science, featuring interactive notebooks on Algorithmic Fairness, Cognitive Biases, Disparate Impact, and P-Hacking.
Garden of forking paths simulator. Hunt for p<0.05 in a null dataset, then see the whole multiverse of 1920 specifications
An adversarial AI referee for empirical economics: finds p-hacking, cherry-picking and specification search, then re-runs the result.
Benchmark for statistically valid AI scientist systems, using audit-closed protocols, transparency logs, and sequential inference to prevent false discoveries in autonomous research agents.
LLM agent for p-hacking & selective-reporting risk screening in academic PDFs.
Will You Spot the Leaks? A Data Science Challenge When models fly too high: A perilous journey through data leakage
Add a description, image, and links to the p-hacking topic page so that developers can more easily learn about it.
To associate your repository with the p-hacking topic, visit your repo's landing page and select "manage topics."