P-values show up in almost every scientific paper, yet they’re one of the most misunderstood ideas in statistics. In this episode, we break from our usual journal-club format to unpack what a p-value really is, why researchers have fought about it for a century, and how that famous 0.05 cutoff became enshrined in science. Along the way, we share stories from our own papers—from a Nature feature that helped reshape the debate to a statistical sleuthing project that uncovered a faulty method in sports science. The result: a behind-the-scenes look at how one statistical tool has shaped the culture of science itself.
Statistical topics
- Bayesian statistics
- Confidence intervals
- Effect size vs. statistical significance
- Fisher’s conception of p-values
- Frequentist perspective
- Magnitude-Based Inference (MBI)
- Multiple testing / multiple comparisons
- Neyman-Pearson hypothesis testing framework
- P-hacking
- Posterior probabilities
- Preregistration and registered reports
- Prior probabilities
- P-values
- Researcher degrees of freedom
- Significance thresholds (p < 0.05)
- Simulation-based inference
- Statistical power
- Statistical significance
- Transparency in research
- Type I error (false positive)
- Type II error (false negative)
- Winner’s Curse
Methodological morals
- “If p-values tell us the probability the null is true, then octopuses are psychic.”
- “Statistical tools don't fool us, blind faith in them does.”
References
- Nuzzo R. Scientific method: statistical errors. Nature. 2014 Feb 13;506(7487):150-2. doi: 10.1038/506150a.
- Nuzzo, R., 2015. Scientists perturbed by loss of stat tools to sift research fudge from fact. Scientific American, pp.16-18.
- Nuzzo RL. The inverse fallacy and interpreting P values. PM&R. 2015 Mar;7(3):311-4. doi: 10.1016/j.pmrj.2015.02.011. Epub 2015 Feb 25.
- Nuzzo, R., 2015. Probability wars. New Scientist, 225(3012), pp.38-41.
- Sainani KL. Putting P values in perspective. PM&R. 2009 Sep;1(9):873-7. doi: 10.1016/j.pmrj.2009.07.003.
- Sainani KL. Clinical versus statistical significance. PM&R. 2012 Jun;4(6):442-5. doi: 10.1016/j.pmrj.2012.04.014.
- McLaughlin MJ, Sainani KL. Bonferroni, Holm, and Hochberg corrections: fun names, serious changes to p values. PM&R. 2014 Jun;6(6):544-6. doi: 10.1016/j.pmrj.2014.04.006. Epub 2014 Apr 22.
- Sainani KL. The Problem with "Magnitude-based Inference". Med Sci Sports Exerc. 2018 Oct;50(10):2166-2176. doi: 10.1249/MSS.0000000000001645.
- Sainani KL, Lohse KR, Jones PR, Vickers A. Magnitude-based Inference is not Bayesian and is not a valid method of inference. Scand J Med Sci Sports. 2019 Sep;29(9):1428-1436. doi: 10.1111/sms.13491.
- Lohse KR, Sainani KL, Taylor JA, Butson ML, Knight EJ, Vickers AJ. Systematic review of the use of "magnitude-based inference" in sports science and medicine. PLoS One. 2020 Jun 26;15(6):e0235318. doi: 10.1371/journal.pone.0235318.
- Wasserstein, R.L. and Lazar, N.A., 2016. The ASA statement on p-values: context, process, and purpose. The American Statistician, 70(2), pp.129-133.
Kristin and Regina’s online courses:
Demystifying Data: A Modern Approach to Statistical Understanding
Clinical Trials: Design, Strategy, and Analysis
Medical Statistics Certificate Program
Writing in the Sciences
Epidemiology and Clinical Research Graduate Certificate Program
Programs that we teach in:
Epidemiology and Clinical Research Graduate Certificate Program
Find us on:
Kristin - LinkedIn & Twitter/X
Regina - LinkedIn & ReginaNuzzo.com
- (00:00) - Intro & claim of the episode
- (01:00) - Why p-values matter in science
- (02:44) - What is a p-value? (ESP guessing game)
- (06:47) - Big vs. small p-values (psychic octopus example)
- (08:29) - Significance thresholds and the 0.05 rule
- (09:00) - Regina’s Nature paper on p-values
- (11:32) - Misconceptions about p-values
- (13:18) - Fisher vs. Neyman-Pearson (history & feud)
- (16:26) - Botox analogy and type I vs. type II errors
- (19:41) - Dating app analogies for false positives/negatives
- (22:02) - How the 0.05 cutoff got enshrined
- (23:46) - Misinterpretations: statistical vs. practical significance
- (25:22) - Effect size, sample size, and “statistically discernible”
- (25:51) - P-hacking and researcher degrees of freedom
- (28:52) - Transparency, preregistration, and open science
- (29:58) - The 0.05 cutoff trap (p = 0.049 vs 0.051)
- (30:24) - The biggest misinterpretation: what p-values actually mean
- (32:35) - Paul the psychic octopus (worked example)
- (35:05) - Why Bayesian statistics differ
- (38:55) - Why aren’t we all Bayesian? (probability wars)
- (40:11) - The ASA p-value statement (behind the scenes)
- (42:22) - Key principles from the ASA white paper
- (43:21) - Wrapping up Regina’s paper
- (44:39) - Kristin’s paper on sports science (MBI)
- (47:16) - What MBI is and how it spread
- (49:49) - How Kristin got pulled in (Christie Aschwanden & FiveThirtyEight)
- (53:11) - Critiques of MBI and “Bayesian monster” rebuttal
- (55:20) - Spreadsheet autopsies (Welsh & Knight)
- (57:11) - Cherry juice example (why MBI misleads)
- (59:28) - Rebuttals and smoke & mirrors from MBI advocates
- (01:02:01) - Winner’s Curse and small samples
- (01:02:44) - Twitter fights & “establishment statistician”
- (01:05:02) - Cult-like following & Matrix red pill analogy
- (01:07:12) - Wrap-up
