Does the temperature of your coffee six months ago really predict whether you feel gassy today? This week we dissect a new nutrition survey study on hot and cold beverage habits that claims to connect drink temperature with gut symptoms, anxiety, and more—despite relying on year-old memories and a blizzard of statistical tests. It’s the perfect case study for our Holiday Survival Guide Part 2, where we teach you how to talk with Uncle Joe at the dinner table about one of the most common—and most fraught—study designs in science: cross-sectional surveys. We walk through our easy checklist for making sense of results, show how recall bias and measurement error can skew the story, and reacquaint you with nonmonogamous Multiple-Testing Dude, who’s been very busy in this dataset. A friendly, practical guide to spotting when researchers are just torturing the data until it confesses.

Statistical topics

• Confounding
• Cross-sectional studies
• False positives
• Measurement error
• Multiple testing
• PICOT / PIVOT framework
• Recall bias
• Research hypotheses
• Sample size and power
• Signal vs. noise
• SMART framework
• Statistical significance
• Subgroup analyses
• Survey design
• Transparency and trustworthiness

Methodological morals

• “When your measurement starts with ‘think back to last winter’ you might as well use a random number generator.”
• “If the effect is only significant in certain subgroups in certain seasons for certain outcomes, it might just be a bad case of gas.”

References

• Wu T, Doyle C, Ito J, et al. Cold Exposures in Relation to Dysmenorrhea among Asian and White Women. Int J Environ Res Public Health. 2023;21(1):56. Published 2023 Dec 30. doi:10.3390/ijerph21010056
• Wu T, Ramesh N, Doyle C, Hsu FC. Cold and hot consumption and health outcomes among US Asian and White populations. Br J Nutr. Published online September 18, 2025. doi:10.1017/S000711452510514X

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
• (04:36) - Did they have real research hypotheses?
• (10:29) - Observational or randomized experiment?
• (20:09) - PICOT and PIVOT
• (26:20) - Memory problems
• (32:03) - Five outcomes and measurement problems therein
• (36:56) - SMART
• (41:50) - Multiple Testing Dude is having a great time
• (52:36) - How big is the effect?
• (59:06) - Wrap-up and Irish Coffee rating scale

Does a little alcohol really make you speak a foreign language better? This week we unpack a quirky randomized trial that tested Dutch pronunciation after a modest buzz—and came to the opposite conclusion the researchers expected. We use it as the perfect holiday case study: instead of arguing with Uncle Joe at the dinner table, we’ll show you how to pull apart a scientific headline using a friendly, practical checklist anyone can learn. Along the way we stress-test the study’s claims, take a quick detour into what a .04% buzz actually looks like, and run our own before-and-after experiment with two brave science journalists at the ScienceWriters2025 conference in Chicago. A holiday survival guide with vodka tonics, statistical sleuthing, and a few surprisingly smooth French phrases.

Statistical topics

• Alternative explanations
• Arithmetic consistency / GRIM test
• Blinding
• Effect size / magnitude
• Generalizability / external validity
• Observational studies vs. experiments
• Outcome measurement
• PICOT framework
• Placebo and expectancy effects
• Primary outcomes / pre-specification
• Randomized controlled trials
• Research hypotheses
• Sample size 
• SMART framework
• Statistical significance (signal vs. noise)
• Transparency and trustworthiness

Methodological morals

• “​​You don't need a PhD to read a study. Just remember, PICOT and SMART.”
• “A decimal point can mean the difference between life and death. Details matter.”

References

• Renner F, Kersbergen I, Field M, Werthmann J. Dutch courage? Effects of acute alcohol consumption on self-ratings and observer ratings of foreign language skills. J Psychopharmacol. 2018;32(1):116-122. doi:10.1177/0269881117735687

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

What do chickenpox and shingles have to do with your brain? This week, we dig into two 2025 headline-grabbing studies that link the shingles shot to lower dementia rates. We start in Wales, where a birthday cutoff turned into the perfect natural experiment, and end in the U.S. with a multi-million-person megastudy. Featuring bias-variance Goldilockses, Fozzy-the-Bear regression discontinuities, a Barbie-versus-Oppenheimer showdown for propensity scores – and the hottest rebrand of inverse-probability weighting you’ll ever hear.

Statistical topics

• Absolute vs. relative risk
• Bias–variance tradeoff
• Causal inference
• Censoring
• Confounding
• Fuzzy regression discontinuity design
• Healthy-user bias
• Inverse probability of treatment weighting (IPTW)
• Longitudinal study
• Natural experiment
• Negative controls
• Optimal bandwidth
• Propensity scores
• Selection bias
• Subgroup analysis
• Triangular kernel weights

Methodological morals

• “Propensity scores are the lipstick you put on observational pigs.”
• “Natural experiments are a hot flirtation date with causality.”

References

• Eyting M, Xie M, Michalik F, Heß S, Chung S, Geldsetzer P. A natural experiment on the effect of herpes zoster vaccination on dementia. Nature. 2025 May;641(8062):438-446. doi: 10.1038/s41586-025-08800-x. Epub 2025 Apr 2. PMID: 40175543; PMCID: PMC12058522.
• Polisky V, Littmann M, Triastcyn A, et al. Varicella-zoster virus reactivation and the risk of dementia. Nat Med. Published online October 6, 2025. doi:10.1038/s41591-025-03972-5
• Sainani KL. Propensity scores: uses and limitations. PM&R 2012; 4:693-97.

Detailed Show Notes Page

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 and first gratuitous mention of sex
• (03:56) - What are shingles, chickenpox, and the vaccines against them?
• (12:30) - Fun facts about the varicella zoster and herpes viruses
• (18:00) - A natural experiment in Wales
• (21:54) - What is the Goldilocks optimal bandwidth?
• (26:17) - Fuzzy regression discontinuity design demystified
• (32:43) - Shingles vaccine vs dementia showdown
• (34:13) - Absolute risk reduction paradox
• (37:44) - Effects for men and women differ
• (41:07) - A giant longitudinal study
• (47:51) - Propensity scores demystified via Barbie and Oppenheimer
• (53:55) - Using propensity scores to make matches
• (58:08) - Inverse probability of treatment weighting demystified via more Barbenheimer
• (01:02:27) - Attempts to rename IPTW for TikTok
• (01:05:59) - Longitudinal study results
• (01:10:00) - Smooch ratings and methodological morals: pigs and hot dates

What if a haunted house makes your date look hotter? This week we dive into the infamous Scary Bridge Study — the 1970s classic that launched a thousand pop-psych takes on fear and lust. It’s the one with the swaying bridge, pretty “research assistant,” and phone number scrawled on torn paper. The study became legend, but how sturdy were its stats? We retrace the design, redo the numbers, and see how many math errors it takes to sway a suspension bridge. Along the way we find an erotic-fiction writing exercise, Adventure Dudes choosing their own experimental groups, and snarky replicators who tried (and failed) to make fear sexy again. We wrap with what the latest research says about when fear really does boost attraction — and when it backfires spectacularly. A Halloween story of danger, desire, and unconscious sexual drive.

This episode has a video version! https://www.youtube.com/watch?v=2coWoS_3460

Statistical topics

• Arithmetic checks
• Chi-square test
• Confounders
• GRIM test
• Inter-rater reliability
• Meta-analysis
• Negative control
• Randomization
• Replication 
• Sample size
• Signal vs. noise
• Statistical sleuthing
• Subjective measurement
• T-test

Methodological morals

• “Those who don't verify their numbers dig their own statistical graves.”
• “Famous doesn't mean flawless.”

References

• Brown, NJ, Heathers, JA. The GRIM test: A simple technique detects numerous anomalies in the reporting of results in psychology. Social Psychological and Personality Science. 2017; 8(4):363-369.
• Dutton DG, Aron AP. Some evidence for heightened sexual attraction under conditions of high anxiety. J Pers Soc Psychol. 1974;30(4):510-517. doi:10.1037/h0037031
• Foster CA, Witcher BS, Campbell WK, Green JD. Arousal and attraction: Evidence for automatic and controlled processes. J Pers Soc Psychol. 1998;74(1):86-101.
• Kenrick DT, Cialdini R, Linder D. Misattribution under fear-producing circumstances: Four failures to replicate. Pers Soc Psychol Bull. 1979;5(3):329-334.
• van der Zee T, Anaya J, Brown NJL. Statistical heartburn: an attempt to digest four pizza publications from the Cornell Food and Brand Lab. BMC Nutr. 2017;3:54. Published 2017 Jul 10. doi:10.1186/s40795-017-0167-x
• http://www.prepubmed.org/grim_test/

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: Fear and Flirtation on a Suspension Bridge
• (05:40) - A Classic 1970s Experiment with No IRB to be Found
• (11:15) - Adventure Dudes Choose Their Own Bridge
• (17:00) - The Sexy Story Scale
• (22:20) - Cool Factor and the Negative Control
• (28:54) - Grim Reaper Math
• (36:29) - T-Tests, Chi-Squares, and Shaky Results
• (42:44) - Electric Shocks and Damsels in Distress
• (50:49) - Replications and Rejections
• (58:39) - Wrap-Up, Methodological Morals, and a New Sexy Rating Scale

Ultramarathoners push their bodies to the limit, but can a giant pre-race dose of vitamin D really keep their bones from breaking down? In this episode, we dig into a trial that tested this claim – and found  a statistical endurance event of its own: six highly interchangeable papers sliced from one small study.  Expect missing runners, recycled figures, and a peer-review that reads like stand-up comedy, plus a quick lesson in using degrees of freedom as your statistical breadcrumbs.

Statistical topics

• Data cleaning and validation
• Degrees of freedom
• Exploratory vs confirmatory analysis
• False positives and Type I error
• Intention-to-treat principle
• Multiple testing
• Open data and transparency
• P-hacking
• Salami slicing
• Parametric vs non-parametric tests
• Peer review quality
• Randomized controlled trials
• Research reproducibility
• Statistical sleuthing

Methodological morals

• “Degrees of freedom are the breadcrumbs in statistical sleuthing. They reveal the sample size even when the authors do not.”
• “Publishing the same study again and again with only the outcomes swapped is Mad Libs Science, better known as salami slicing.”
  
  

References

• Boswell, Rachel. Pre-race vitamin D could do wonders for ultrarunners’ bone health, according to science. Runner’s World. September 25, 2025. 
• Mieszkowski J, Stankiewicz B, Kochanowicz A, et al. Ultra-Marathon-Induced Increase in Serum Levels of Vitamin D Metabolites: A Double-Blind Randomized Controlled Trial. Nutrients. 2020;12(12):3629. Published 2020 Nov 25. doi:10.3390/nu12123629
• Mieszkowski J, Borkowska A, Stankiewicz B, et al. Single High-Dose Vitamin D Supplementation as an Approach for Reducing Ultramarathon-Induced Inflammation: A Double-Blind Randomized Controlled Trial. Nutrients. 2021;13(4):1280. Published 2021 Apr 13. doi:10.3390/nu13041280
• Mieszkowski J, Brzezińska P, Stankiewicz B, et al. Direct Effects of Vitamin D Supplementation on Ultramarathon-Induced Changes in Kynurenine Metabolism. Nutrients. 2022;14(21):4485. Published 2022 Oct 25. doi:10.3390/nu14214485
• Mieszkowski J, Brzezińska P, Stankiewicz B, et al. Vitamin D Supplementation Influences Ultramarathon-Induced Changes in Serum Amino Acid Levels, Tryptophan/Branched-Chain Amino Acid Ratio, and Arginine/Asymmetric Dimethylarginine Ratio. Nutrients. 2023;15(16):3536. Published 2023 Aug 11. doi:10.3390/nu15163536
• Stankiewicz B, Mieszkowski J, Kochanowicz A, et al. Effect of Single High-Dose Vitamin D3 Supplementation on Post-Ultra Mountain Running Heart Damage and Iron Metabolism Changes: A Double-Blind Randomized Controlled Trial. Nutrients. 2024;16(15):2479. Published 2024 Jul 31. doi:10.3390/nu16152479
• Stankiewicz B, Kochanowicz A, et al. Single high-dose vitamin D supplementation impacts ultramarathon-induced changes in serum levels of bone turnover markers: a double-blind randomized controlled trial. J Int Soc Sports Nutr. 2025 Dec;22(1):2561661. doi: 10.1080/15502783.2025.2561661.

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
 00:44 Runner’s World headline: Vitamin D for ultramarathoners
 02:03 Kristin’s connection to running and vitamin D skepticism
 03:32 Ultramarathon world—Regina’s stories and Death Valley race
 06:29 What ultramarathons do to your bones
 08:02 Boy story: four stress fractures in one race
 10:00 Study design—40 male runners in Poland
 11:33 Missing flow diagram and violated intention-to-treat
 13:02 The intervention: 150,000 IU megadose
 15:09 Blinding details and missing randomization info
 17:13 Measuring bone biomarkers—no primary outcome specified
 19:12 The wrong clinicaltrials.gov registration
 20:35 Discovery of six papers from one dataset (salami slicing)
 23:02 Why salami slicing misleads readers
 25:42 Inconsistent reporting across papers
 29:11 Changing inclusion criteria and sloppy methods
 31:06 Typos, Polish notes, and misnumbered references
 32:39 Peer review comedy gold—“Please define vitamin D”
 36:06 Reviewer laziness and p-hacking admission
 39:13 Results: implausible bone growth mid-race
 41:16 Degrees of freedom sleuthing reveals hidden sample sizes
 47:07 Open data? Kristin emails the authors
 48:42 Lessons from Kristin’s own ultramarathon dataset
 51:22 Fishing expeditions and misuse of parametric tests
 53:07 Strength of evidence: one smooch each
 54:44 Methodologic morals—Mad Libs Science & degrees of freedom breadcrumbs
 56:12 Anyone can spot red flags—trust your eyes
 57:34 Outro: skip the vitamin D shot before your next run 

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
• (24:40) - Misinterpretations: statistical vs. practical significance
• (26:16) - Effect size, sample size, and “statistically discernible”
• (26:45) - P-hacking and researcher degrees of freedom
• (29:46) - Transparency, preregistration, and open science
• (30:52) - The 0.05 cutoff trap (p = 0.049 vs 0.051)
• (31:18) - The biggest misinterpretation: what p-values actually mean
• (33:29) - Paul the psychic octopus (worked example)
• (35:59) - Why Bayesian statistics differ
• (39:49) - Why aren’t we all Bayesian? (probability wars)
• (41:05) - The ASA p-value statement (behind the scenes)
• (43:16) - Key principles from the ASA white paper
• (44:15) - Wrapping up Regina’s paper
• (45:33) - Kristin’s paper on sports science (MBI)
• (48:10) - What MBI is and how it spread
• (50:43) - How Kristin got pulled in (Christie Aschwanden & FiveThirtyEight)
• (54:05) - Critiques of MBI and “Bayesian monster” rebuttal
• (56:14) - Spreadsheet autopsies (Welsh & Knight)
• (58:05) - Cherry juice example (why MBI misleads)
• (01:00:22) - Rebuttals and smoke & mirrors from MBI advocates
• (01:02:55) - Winner’s Curse and small samples
• (01:03:38) - Twitter fights & “establishment statistician”
• (01:05:56) - Cult-like following & Matrix red pill analogy
• (01:08:06) - Wrap-up

Exercise has long been hailed as cancer-fighting magic, but is there hard evidence behind the hype? In this episode, we tackle the CHALLENGE trial, a large phase III study of colon cancer patients that tested whether prescribed exercise could improve cancer-free survival. We translate clinical jargon into plain English, show why ratio statistics make splashy headlines while absolute differences tell the real story, and take a detour into why statisticians think survival analysis is downright sexy. And we even bring in a classic reality show to make sense of the numbers.

Statistical topics

• Data and Safety Monitoring Board (DSMB)
• Hazard ratios
• Intention-to-treat analysis
• Interim analyses
• Kaplan-Meier curves
• Phase III trials
• Randomized clinical trial
• Rates and rate ratios
• Relative vs absolute differences
• Stratified randomization with minimization
• Survival analysis
• Time-to-event variables

Methodological morals

• “Ratio statistics sell headlines. Absolute differences sell truth.”
• “Survival analysis is this sexy stats tool that makes every moment and every Cox count.”

References

• Courneya KS, Vardy JL, O'Callaghan CJ, et al. Structured Exercise after Adjuvant Chemotherapy for Colon Cancer. NEJM. 2025;393:13-25. 
• Rabin RC. Are Marathons and Extreme Running Linked to Colon Cancer? The New York Times. Aug 19, 2025.
• Sainani KL. Introduction to survival analysis. PM&R. 2016;  8:580-85.
• Sainani KL. Making sense of intention-to-treat. PM&R. 2010;2:209-13.

Thanks

Thanks to Caitlin Goodrich for the episode topic tip!

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
• (05:42) - Two different types of cancer studies
• (08:12) - Why might exercise affect cancer?
• (10:05) - Phase III trials are different
• (12:40) - Who was in the CHALLENGE trial?
• (13:31) - Stratified randomization with minimization
• (15:05) - The exercise prescription
• (19:17) - What did the CHALLENGE trial measure?
• (20:04) - Disease-free survival
• (21:59) - Data and Safety Monitoring Board – what do they do?
• (24:35) - Participants and adherence to exercise
• (26:54) - Intention-to-treat analysis
• (29:58) - Survival analysis overview
• (31:51) - Kaplan-Meier curves
• (34:27) - Reality-show analogy
• (36:54) - Ratio statistics are confusing
• (39:30) - Hazard ratios
• (47:03) - Wrap-up, rating, and methodological morals

Are we all secretly ageist when it comes to dating? We put the stereotype that older men prefer younger women under the microscope using data from thousands of blind dates. What we found surprised us: the “age penalty” was real but microscopic, women wanted younger partners too, and hard age cutoffs weren’t so hard after all. Along the way, we unpack statistical significance versus practical importance, play with the infamous “half your age plus seven” rule, and imagine what it would take for love to die out… somewhere around age 628.

Statistical topics

• Discontinuous regression
• Effect sizes
• Extrapolation pitfalls
• Linear regression
• Logistic regression
• Odds ratios
• Open data
• Statistical significance vs. practical significance

Methodological morals

• “Do not be swept off your feet by statistical significance. Tiny effects in bed are still tiny.”
• “Fancy units sound smart, but plain English wins hearts.”

Show Notes Technical Appendix (with step-by-step explanations)

References

• Eastwick PW, Finkel EJ, Meza EM, Ammerman K. No gender differences in attraction to young partners: A study of 4500 blind dates. Proc Natl Acad Sci U S A. 2025 Feb 4;122(5):e2416984122. 
• Matchmaking Dataset and Code on Open Science Framework: https://osf.io/rkm2d/?view_only=a0fe91dae0464077af7772e6890a8151
• Nuzzo RL. Communicating measures of relative risk in plain English. PM&R. 2022 Feb;14(2):283-7.
• O'Rell, Max. Her Royal Highness, Woman: And His Majesty--Cupid. Abbey Press, 1901.
• Sainani KL. Logistic regression. PM&R. 2014 Dec;6(12):1157-62.
• Sainani KL. Understanding odds ratios. PM&R. 2011 Mar;3:263-7. 
• Sainani KL. Clinical versus statistical significance. PM&R. 2012 Jun;4:442-5.

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
• (04:01) - Half-your-age-plus-seven rule
• (09:15) - Matchmaking service for the study
• (17:59) - Blind dates as natural experiments
• (22:49) - Regression results part 1: Age penalties?
• (29:32) - Wait, how big of an effect was that?
• (35:03) - Odds ratio of a second date
• (38:55) - Surprising age pair-ups
• (41:47) - Regression results part 2: Deal-breaking age limits?
• (45:21) - Why the patterns may or may not be true
• (47:24) - Wrap-up, ratings, and methodological morals

ChatGPT is melting our brainpower, killing creativity, and making us soulless — or so the headlines imply. We dig into the study behind the claims, starting with quirky bar charts and mysterious sample sizes, then winding through hairball-like brain diagrams and tens of thousands of statistical tests. Our statistical sleuthing leaves us with questions, not just about the results, but about whether this was science’s version of a first date that looked better on paper.

Statistical topics

• ANOVA
• Bar graphs
• Data visualization 
• False Discovery Rate correction
• Multiple testing
• Preprints
• Statistical Sleuthing

Methodological morals

• "Treat your preprints like your blind dates. Show up showered and with teeth brushed."
• "Always check your N. Then check it again."
• "Never make a bar graph that just shows p-values. Ever."

Link to paper

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
• (03:46) - Media coverage of the study
• (08:35) - The experiment
• (12:09) - Sample size issues
• (13:11) - Bar chart sleuthing
• (19:15) - Blind date analogy
• (23:51) - Interview results
• (30:01) - Simple text analysis results
• (34:01) - Natural language processing results
• (40:57) - N-gram and ontology analysis results
• (45:52) - Teacher evaluation results
• (52:27) - Neuroimaging analysis
• (01:00:29) - Multiple testing and connectivity issues
• (01:06:07) - Brain adaptation results
• (01:09:44) - Wrap-up, rating, and methodological morals

Can correcting misinformation make it worse? The “backfire effect” claims that debunking myths can actually make false beliefs stronger. We dig into the evidence — from ghost studies to headline-making experiments — to see if this psychological plot twist really holds up. Along the way, we unpack interaction effects, randomization red flags, and what happens when bad citations take on a life of their own. Plus: dirty talk analogies, statistical sleuthing, and why “familiarity” might be your brain’s sneakiest trick.

Statistical topics

• Computational replication
• Replication
• Block randomization
• Problems in randomization
• Bad citing
• Interactions in regression

Unpublished "Ghost Paper"

• PDF retrieved from the Wayback Machine

Citations

• Nyhan B, Reifler J. When corrections fail: The persistence of political misperceptions. Political Behavior. 2010;32:303–330.
• Skurnik I, Yoon C, Schwarz N. “Myths & Facts” about the flu: Health education campaigns can reduce vaccination intentions. Unpublished manuscript, PDF posted separately.
• Schwarz N, Sanna LJ, Skurnik I, et al. Metacognitive experiences and the intricacies of setting people straight: Implications for debiasing and public information campaigns. Advances in Experimental Social Psychology. 2007;39:127–61.
• Lewandowsky S, Ecker UKH, Seifert CM, et al. Misinformation and its correction: Continued influence and successful debiasing. Psychological Science in the Public Interest. 2012;13:106–131.
• Pluviano S, Watt C, Della Sala S. Misinformation lingers in memory: Failure of three pro-vaccination strategies. PLOS ONE. 2017;12:e0181640.
• Pluviano S, Watt C, Ragazzini G, et al. Parents’ beliefs in misinformation about vaccines are strengthened by pro‑vaccine campaigns. Cognitive Processing. 2019;20:325–31.
• Wood T, Porter E. The elusive backfire effect: Mass attitudes’ steadfast factual adherence. Political Behavior. 2019;41:135–63.
• Nyhan B, Porter E, Reifler J, Wood TJ. Taking fact-checks literally but not seriously? The effects of journalistic fact-checking on factual beliefs and candidate favorability. Political Behavior. 2020;42:939–60.
• Ecker UKH, Hogan JL, Lewandowsky S. Reminders and repetition of misinformation: Helping or hindering its retraction? Journal of Applied Research in Memory and Cognition. 2017;6:185–92.
• Swire B, Ecker UKH, Lewandowsky S. The role of familiarity in correcting inaccurate information. Journal of Experimental Psychology: Learning, Memory, and Cognition. 2017;43:1948–61.
• Ecker UKH, O’Donnell M, Ang LC, et al. The effectiveness of short- and long-format retractions on misinformation belief and recall. British Journal of Psychology. 2020;111:36–54.
• Ecker UKH, Sharkey CXM, Swire-Thompson B. Correcting vaccine misinformation: A failure to replicate familiarity or fear-driven backfire effects. PLOS ONE. 2023;18:e0281140.
• Cook J, Lewandowsky S. The Debunking Handbook. University of Queensland. 2011.
• Lewandowsky S, Cook J, Ecker UKH, et al. The Debunking Handbook 2020. Available at https://sks.to/db2020. 
• Swire‑Thompson B, DeGutis J, Lazer D. Searching for the backfire effect: Measurement and design considerations. Journal of Applied Research in Memory and Cognition. 2020;9:286–99.

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) -
• (00:00) - Intro
• (02:05) - What is the backfire effect?
• (03:55) - The 2010 paper that panicked fact-checkers
• (06:25) - The ghost paper what it really said
• (12:35) - Study design of the 2010 paper
• (19:19) - Results of the 2010 paper
• (20:49) - Crossover interactions, regression models, and intimate talk
• (26:18) - Missing data and cleaning your bedroom analogy
• (29:05) - Fact-checking the fact-checking paper
• (34:01) - Replication and pushing the data to the limit
• (37:53) - The purported backfire effect spreads
• (42:00) - The 2017 paper that got a lot of attention
• (45:19) - Statistical sleuthing the 2017 paper
• (49:45) - Will researchers double down on their earlier conclusions?
• (55:40) - A review paper sums it all up
• (56:54) - Wrap up, rating, and methodological morals

Loyal, funny, hot — you’ve probably got a wish list for your dream partner. But does checking all your boxes actually lead to happily ever after? In this episode, we dive into a massive global study that put the “ideal partner” hypothesis to the test. Do people really know what they want, and does getting it actually make them happier? We explore surprising statistical insights from over 10,000 romantics in 43 countries, from mean-centering and interaction effects to the good-catch confounder. Along the way, we dig into dessert metaphors, partner boat-count regression models, and the one trait that people say doesn’t matter — but secretly makes them happiest.

Statistical topics

• Regression
• Random Slopes and Intercepts (Random Effects) in Regression
• Standardized Beta Coefficients in Regression
• Interaction Effects in Regression
• Mean Centering
• Exploratory Analyses

Methodological morals

“Good science bares it all.”

“When the world isn't one size fits all, don't fit just one line; use random slopes and intercepts.”

References

• Eastwick PW, Sparks J, Finkel EJ, Meza EM, Adamkovič M, Adu P, Ai T, Akintola AA, Al-Shawaf L, Apriliawati D, Arriaga P, Aubert-Teillaud B, Baník G, Barzykowski K, Batres C, Baucom KJ, Beaulieu EZ, Behnke M, Butcher N, Charles DY, Chen JM, Cheon JE, Chittham P, Chwiłkowska P, Cong CW, Copping LT, Corral-Frias NS, Ćubela Adorić V, Dizon M, Du H, Ehinmowo MI, Escribano DA, Espinosa NM, Expósito F, Feldman G, Freitag R, Frias Armenta M, Gallyamova A, Gillath O, Gjoneska B, Gkinopoulos T, Grafe F, Grigoryev D, Groyecka-Bernard A, Gunaydin G, Ilustrisimo R, Impett E, Kačmár P, Kim YH, Kocur M, Kowal M, Krishna M, Labor PD, Lu JG, Lucas MY, Małecki WP, Malinakova K, Meißner S, Meier Z, Misiak M, Muise A, Novak L, O J, Özdoğru AA, Park HG, Paruzel M, Pavlović Z, Püski M, Ribeiro G, Roberts SC, Röer JP, Ropovik I, Ross RM, Sakman E, Salvador CE, Selcuk E, Skakoon-Sparling S, Sorokowska A, Sorokowski P, Spasovski O, Stanton SCE, Stewart SLK, Swami V, Szaszi B, Takashima K, Tavel P, Tejada J, Tu E, Tuominen J, Vaidis D, Vally Z, Vaughn LA, Villanueva-Moya L, Wisnuwardhani D, Yamada Y, Yonemitsu F, Žídková R, Živná K, Coles NA. A worldwide test of the predictive validity of ideal partner preference matching. J Pers Soc Psychol. 2025 Jan;128(1):123-146. doi: 10.1037/pspp0000524
• Love Factually Podcast: https://www.lovefactuallypod.com/

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) -
• (00:00) - Intro
• (04:57) - Actual dating profile wishlists vs study wishlists
• (09:12) - Juicy paper details
• (18:31) - What the study actually asked – wishlist, partner resume, relationship satisfaction
• (25:04) - Linear regression illustrated through number of boats your partner has
• (31:31) - Standardized regression coefficients illustrated through spouse height concordance
• (35:46) - Good catch confounder: We all just want the same high-quality ice cream / mate
• (40:40) - Does your personalized wishlist matter? Results
• (42:55) - Wishlist regression interaction effects: like chocolate and peanut butter
• (46:45) - Partner traits result in happiness bonus points
• (50:45) - What do we say we want – and what really makes us happy? Surprise
• (55:04) - Gender stereotypes and whether they held up
• (57:45) - Random effects models and boats again
• (01:00:24) - Other cool things they did
• (01:01:35) - One-minute paper summary
• (01:03:17) - Wrap-up, rate the claim, methodological morals