Normal Curves: Sexy Science, Serious Statistics

P-Values: Are we using a flawed statistical tool?

September 22, 2025 · 1 h 13 min

Show notes

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.