bullshit-detector 0.2.0

Statistical detection tools for screening published research

bullshit-detector

Statistical detection tools for screening published research.

"Bullshit is unavoidable whenever circumstances require someone to talk without knowing what they are talking about." — Harry Frankfurt, On Bullshit (2005)

What this is

A Python toolkit for systematically screening research papers for statistical red flags. Organized in four tiers, from quick API lookups to deep data analysis. Developed from petroleum geoscience applications but applicable to any field where correlations, p-values, and sample sizes are reported.

Tier	What it checks	What you need	Time
0 — Paper screening	Journal legitimacy, retractions, author credentials	DOI, journal name	Minutes
1 — Arithmetic	p-value consistency, GRIM/GRIMMER tests	Reported statistics	Minutes
2 — Plausibility	Spurious correlations, critical r, confidence intervals	Summary stats (r, n, k)	Minutes
3 — Data analysis	Outlier leverage, distance correlation, reproducibility	Raw/digitized data	Hours

Installation

pip install bullshit-detector          # Core (Tiers 0–2)
pip install bullshit-detector[full]    # + Tier 3 tools (statsmodels, seaborn)
pip install bullshit-detector[batch]   # + statcheck for PDF batch scanning (GPL-3.0)
pip install bullshit-detector[dev]     # + pytest for development

Quick Start

Is the reported p-value correct?

from bullshit_detector.p_checker import check_p_value

check_p_value("t", 2.20, 28, reported_p=0.04)
# {'computed_p': 0.0362254847788378, 'reported_p': 0.04,
#  'consistent': True, 'decision_error': False, ...}

Reported p=0.04 is consistent with computed p=0.036 — within rounding tolerance.

Could this correlation be spurious?

from bullshit_detector.spurious import P_spurious

# r=0.60, n=5 observations, k=10 predictor variables tested
P_spurious(0.60, 5, 10)
# 0.9649622440458044

With r=0.60, n=5 observations, and k=10 variables tested, there is a 96.5% probability this correlation is spurious.

Has this paper been retracted?

from bullshit_detector.paper_screening import check_retraction

check_retraction("10.2147/DMSO.S27665")
# {'retracted': True, 'corrections': [], 'pubpeer_comments': 0,
#  'pubpeer_url': 'https://pubpeer.com/publications/10.2147-DMSO.S27665'}

retracted: True — the paper behind this DOI has been formally retracted. Do not cite or act on its findings. (This is the Vinson et al. 2012 green coffee extract paper, retracted in 2014 after an FTC investigation.)

Intellectual foundations

This project stands on the shoulders of:

• Carl T. Bergstrom & Jevin D. West — Calling Bullshit: The Art of Skepticism in a Data-Driven World (Random House, 2020) and their University of Washington course. The paper-level screening module (Tier 0) directly implements their legitimacy framework.
• Harry Frankfurt — On Bullshit (Princeton University Press, 2005). Established the philosophical foundation.
• C.T. Kalkomey — "Potential risks when using seismic attributes as predictors of reservoir properties" (The Leading Edge, 1997). The spurious correlation probability formula.
• N.J.L. Brown & J.A.J. Heathers — "The GRIM Test" (SPPS, 2017). Arithmetic consistency checking for means.
• Aurélien Allard — "Analytic-GRIMMER" (2018). Extended GRIM to standard deviations. The Python port in this package is the first on PyPI.
• Kristin Sainani — "How to Be a Statistical Detective" (PM&R, 2020). Pedagogical framework tying these tools together.
• Thomas Speidel — GeoConvention 2018 R notebook. Variable selection methods (redundancy analysis, LASSO, sparse PCA, power analysis) on the Hunt dataset, complementing Niccoli's Python implementations. Inspired the redundancy and power modules.
• Michèle Nuijten et al. — statcheck (Behavioral Research Methods, 2016). P-value recomputation methodology.

For AI assistants

The skills/ directory contains detection heuristics and decision trees for each module. If you're a coding assistant (Copilot, Claude Code, etc.), read skills/OVERVIEW.md first.

Acknowledgments

Kristin Sainani — her paper "How to Be a Statistical Detective" (PM&R, 2020, 12(2):211–215, DOI: 10.1002/pmrj.12305) inspired the Tier 1 arithmetic consistency approach and the overall "statistical detective" framing of this package. The p_checker module's pedagogical structure follows her framework of treating statistical anomalies as clues that warrant further investigation. More broadly, her detective framing runs through the entire package lineage: it directly inspired Matteo Niccoli's "Be a geoscience and data science detective" project (MyCarta blog, GitHub repo, TRANSFORM 2021 lightning talk), which is the primary source for the Tier 3 modules (leverage.py, reproducibility.py).

Thomas Speidel — his GeoConvention 2018 R notebook, Data Science Tools for Petroleum Exploration and Production, provided the methodology for the power analysis and redundancy modules (Tier 2). The original GeoConvention 2018 presentation was a collaboration between Matteo Niccoli and Thomas Speidel; Speidel's R implementations of power analysis and variable redundancy (Hmisc::redun), applied to the Hunt (2013) 21-well dataset, were translated into the Python power and redundancy modules in this package.

License

Apache-2.0. The optional statcheck dependency is GPL-3.0.