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Normal Curves: Sexy Science, Serious Statistics

Regina Nuzzo and Kristin Sainani
Normal Curves: Sexy Science, Serious Statistics
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  • Age Gaps: How much does age matter in dating?
    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 topicsDiscontinuous regressionEffect sizesExtrapolation pitfallsLinear regressionLogistic regressionOdds ratiosOpen dataStatistical significance vs. practical significanceMethodological 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)ReferencesEastwick 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=a0fe91dae0464077af7772e6890a8151Nuzzo 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/XRegina - LinkedIn & ReginaNuzzo.com(00:00) - Intro (04:01) - Half-your-age-plus-seven rule (09:15) - Matchmaking service for the study (17:05) - Blind dates as natural experiments (21:55) - Regression results part 1: Age penalties? (28:38) - Wait, how big of an effect was that? (34:09) - Odds ratio of a second date (38:01) - Surprising age pair-ups (40:53) - Regression results part 2: Deal-breaking age limits? (44:27) - Why the patterns may or may not be true (46:30) - Wrap-up, ratings, and methodological morals
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  • Your Brain on AI: Is ChatGPT making us mentally lazy?
    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 topicsANOVABar graphsData visualization False Discovery Rate correctionMultiple testingPreprintsStatistical SleuthingMethodological 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 paperKristin 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 ProgramPrograms that we teach in:Epidemiology and Clinical Research Graduate Certificate Program Find us on:Kristin -  LinkedIn & Twitter/XRegina - 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 (22:57) - Interview results (29:07) - Simple text analysis results (33:07) - Natural language processing results (40:03) - N-gram and ontology analysis results (44:58) - Teacher evaluation results (51:33) - Neuroimaging analysis (59:35) - Multiple testing and connectivity issues (01:05:13) - Brain adaptation results (01:08:50) - Wrap-up, rating, and methodological morals
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  • The Backfire Effect: Can fact-checking make false beliefs stronger?
    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 topicsComputational replicationReplicationBlock randomizationProblems in randomizationBad citingInteractions in regressionUnpublished "Ghost Paper"PDF retrieved from the Wayback MachineCitationsNyhan 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/XRegina - 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 (18:25) - Results of the 2010 paper (19:55) - Crossover interactions, regression models, and intimate talk (25:24) - Missing data and cleaning your bedroom analogy (28:11) - Fact-checking the fact-checking paper (33:07) - Replication and pushing the data to the limit (36:59) - The purported backfire effect spreads (41:06) - The 2017 paper that got a lot of attention (44:25) - Statistical sleuthing the 2017 paper (48:51) - Will researchers double down on their earlier conclusions? (54:46) - A review paper sums it all up (56:00) - Wrap up, rating, and methodological morals
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  • Dating Wishlists: Are we happier when we get what we want in a mate?
    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 topicsRegressionRandom Slopes and Intercepts (Random Effects) in RegressionStandardized Beta Coefficients in RegressionInteraction Effects in RegressionMean CenteringExploratory AnalysesMethodological 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.”ReferencesEastwick 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/pspp0000524Love 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/XRegina - 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 (24:10) - Linear regression illustrated through number of boats your partner has (30:37) - Standardized regression coefficients illustrated through spouse height concordance (34:52) - Good catch confounder: We all just want the same high-quality ice cream / mate (39:46) - Does your personalized wishlist matter? Results (42:01) - Wishlist regression interaction effects: like chocolate and peanut butter (45:51) - Partner traits result in happiness bonus points (49:51) - What do we say we want – and what really makes us happy? Surprise (54:10) - Gender stereotypes and whether they held up (56:51) - Random effects models and boats again (59:30) - Other cool things they did (01:00:41) - One-minute paper summary (01:02:23) - Wrap-up, rate the claim, methodological morals
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  • Stats Reunion: What have we learned so far?
    It’s our first stats reunion! In this special review episode, we revisit favorite concepts from past episodes—p-values, multiple testing, regression adjustment—and give them fresh personalities as characters. Meet the seductive false positive, the clingy post hoc ex, and Charlotte, the well-meaning but overfitting idealist.Statistical topicsBar charts vs Box plotsBonferroni correctionConfoundingFalse positives Multiple testingMultivariable regressionOutcome switchingOver-adjustmentPost hoc analysisPre-registrationResidual confoundingStatistical adjustment using regressionSubgroup analysis Unmeasured confoundingReview SheetReferencesNuzzo RL. The Box Plots Alternative for Visualizing Quantitative Data. PM R. 2016 Mar;8(3):268-72. doi: 10.1016/j.pmrj.2016.02.001. Epub 2016 Feb 15. PMID: 26892802.Sainani KL. The problem of multiple testing. PM R. 2009 Dec;1(12):1098-103. doi: 10.1016/j.pmrj.2009.10.004. PMID: 20006317.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/XRegina - LinkedIn & ReginaNuzzo.com(00:00) - Intro (02:26) - Mailbag (06:42) - P-values (12:43) - Multiple Testing Guy (16:05) - Bonferroni solution (17:11) - Post hoc analysis ex (22:22) - Subgroup analysis person (29:34) - Statistical adjustment idealist (43:00) - Unmeasured confounding (44:25) - Residual confounding (48:31) - Over-adjustment (53:48) - Wrap-up
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About Normal Curves: Sexy Science, Serious Statistics

Normal Curves is a podcast about sexy science & serious statistics. Ever try to make sense of a scientific study and the numbers behind it? Listen in to a lively conversation between two stats-savvy friends who break it all down with humor and clarity. Professors Regina Nuzzo of Gallaudet University and Kristin Sainani of Stanford University discuss academic papers journal club-style — except with more fun, less jargon, and some irreverent, PG-13 content sprinkled in. Join Kristin and Regina as they dissect the data, challenge the claims, and arm you with tools to assess scientific studies on your own.
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