Normal Curves: Sexy Science, Serious Statistics

Regina Nuzzo and Kristin Sainani
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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.

  1. Holiday Survival Guide: How to talk about scientific studies around the dinner table

    3日前

    Holiday Survival Guide: How to talk about scientific studies around the dinner table

    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 explanationsArithmetic consistency / GRIM testBlindingEffect size / magnitudeGeneralizability / external validityObservational studies vs. experimentsOutcome measurementPICOT frameworkPlacebo and expectancy effectsPrimary outcomes / pre-specificationRandomized controlled trialsResearch hypothesesSample size SMART frameworkStatistical 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

    1時間2分
  2. Shingles Shot and Dementia: Could one vaccine protect your brain?

    11月3日

    Shingles Shot and Dementia: Could one vaccine protect your brain?

    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 riskBias–variance tradeoffCausal inferenceCensoringConfoundingFuzzy regression discontinuity designHealthy-user biasInverse probability of treatment weighting (IPTW)Longitudinal studyNatural experimentNegative controlsOptimal bandwidthPropensity scoresSelection biasSubgroup analysisTriangular 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/s41591-025-03972-5Sainani 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

    1時間13分
  3. Scary Bridge Study: Can fear make you horny?

    10月20日

    Scary Bridge Study: Can fear make you horny?

    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 checksChi-square testConfoundersGRIM testInter-rater reliabilityMeta-analysisNegative controlRandomizationReplication Sample sizeSignal vs. noiseStatistical sleuthingSubjective measurementT-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/h0037031Foster 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-xhttp://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

    1時間5分
  4. Ultramarathons: Can vitamin D protect your bones?

    10月6日

    Ultramarathons: Can vitamin D protect your bones?

    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 validationDegrees of freedomExploratory vs confirmatory analysisFalse positives and Type I errorIntention-to-treat principleMultiple testingOpen data and transparencyP-hackingSalami slicingParametric vs non-parametric testsPeer review qualityRandomized controlled trialsResearch reproducibilityStatistical 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/nu12123629Mieszkowski 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/nu13041280Mieszkowski 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/nu14214485Mieszkowski 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/nu15163536Stankiewicz 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/nu16152479Stankiewicz 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

    59分
  5. P-Values: Are we using a flawed statistical tool?

    9月22日

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

    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 statisticsConfidence intervals Effect size vs. statistical significanceFisher’s conception of p-valuesFrequentist perspectiveMagnitude-Based Inference (MBI)Multiple testing / multiple comparisonsNeyman-Pearson hypothesis testing frameworkP-hackingPosterior probabilitiesPreregistration and registered reportsPrior probabilitiesP-valuesResearcher degrees of freedomSignificance thresholds (p 0.05)Simulation-based inferenceStatistical power Statistical significanceTransparency 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

    1時間14分
  6. Exercise and Cancer: Does physical activity improve colon cancer survival?

    9月8日

    Exercise and Cancer: Does physical activity improve colon cancer survival?

    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 ratiosIntention-to-treat analysisInterim analysesKaplan-Meier curvesPhase III trialsRandomized clinical trialRates and rate ratiosRelative vs absolute differencesStratified randomization with minimizationSurvival analysisTime-to-event variablesMethodological 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

    50分

  7. 8月25日

    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 topics Discontinuous regressionEffect sizesExtrapolation pitfallsLinear regressionLogistic regressionOdds ratiosOpen dataStatistical 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=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/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

    51分
  8. Your Brain on AI: Is ChatGPT making us mentally lazy?

    8月11日

    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 topics ANOVABar 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 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 ProgramPrograms 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

    1時間15分
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予告編

番組について

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.

情報

  • クリエイター
    Regina Nuzzo and Kristin Sainani
  • 配信期間
    25年
  • エピソード
    22
  • 制限指定
    不適切な内容を含む
  • Copyright
    © 2025 Regina Nuzzo & Kristin Sainani
  • 番組のWebサイト
    Normal Curves: Sexy Science, Serious Statistics

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