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