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Artwork for Normal Curves: Sexy Science, Serious Statistics
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Normal Curves: Sexy Science, Serious Statistics

Regina Nuzzo and Kristin Sainani

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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  • #38
    August 24 · 48 min

    Cannabis and Brain Aging: Can weed keep your brain young?

    Can weed protect your brain as you get older? We unpack a study linking lifetime cannabis use to larger brain volumes and better cognitive test scores in older adults—and investigate how those findings became headlines urging readers to “pass the pot to grandma.” Along the way, we explore collapsing categorical variables, confounding, clinically versus statistically significant results, multiple testing, and how a press release subtly changed a story. We also meet permanently stoned mice, learn about Carl Sagan’s surprising connection to cannabis research, and discover how a single missing word can change the entire meaning of a study. Statistical topics Categorical variables Confounding Effect size False discovery rate Healthy volunteer bias Measurement error Multiple testing Observational studies Recoding variables Science communication Statistical vs practical significance Methodologic Morals “Always follow the chain from data to result to conclusions and pay attention to the words along the way.” “Huge samples can give tiny differences delusions of grandeur.” References Guha A, Fu Z, Calhoun V, Hutchison KE. Lifetime Cannabis Use Is Associated With Brain Volume and Cognitive Function in Middle-Aged and Older Adults. J Stud Alcohol Drugs. 2026;87(4):739-751. doi:10.15288/jsad.25-00346 Press Release: https://news.cuanschutz.edu/news-stories/study-finds-cannabis-usage-in-middle-aged-and-older-adults-associated-with-larger-brain-volume-better-cognitive-function Bilkei-Gorzo A, Albayram O, Draffehn A, et al. A chronic low dose of Δ9-tetrahydrocannabinol (THC) restores cognitive function in old mice. Nat Med. 2017;23(6):782-787. doi:10.1038/nm.4311 NY Post: https://nypost.com/2026/02/07/health/cannabis-may-benefit-aging-brains-study-finds/ David Grinspoon: https://www.nytimes.com/2020/07/02/science/lester-grinspoon-dead.html?unlocked_article_code=1.7lA.CyS1.neeqGGNgx8g1&smid=url-share 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) - Opening: The Brain on Drugs (00:37) - The Cannabis Headline (03:38) - The Claim: Brain Benefits for Older Adults (06:53) - The Mouse Study: Pothead Mice vs. Control Mice (11:25) - From Mice to Humans: UK Biobank (15:01) - Measurement Problems and Category Collapse (21:39) - Results: Statistically Significant, Clinically Meaningless (27:13) - Individual Brain Regions and Cherry-Picking (32:32) - Cognitive Tests: What Actually Matters (40:09) - How It Gets Lost in Translation (45:32) - Rating the Claim

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  • #37
    August 10 · 40 min

    Double Duty: Can the meningitis vaccine also protect against gonorrhea?

    Can the meningitis vaccine do double duty against gonorrhea? We examine a fascinating randomized trial inspired by years of encouraging observational studies—and what happened when those earlier results were finally put to the test. Along the way, we discuss ecological studies, case-control studies, why statisticians distrust phrases like “trend toward significance,” how intention-to-treat differs from per-protocol analysis, and why no amount of statistical adjustment can fully substitute for randomization. Along the way we celebrate outrageously named clinical trials, ask whether “flirting with statistical significance” belongs in a romance novel or a medical journal, and discover that “practice safe statistics” may be the best advice of the episode. Statistical topics Case-control studies Confounding Ecological studies Generalizability Intention-to-treat Meta-analysis Null results Observational studies P-values Per-protocol analysis Randomized clinical trials Survival analysis Methodologic Morals “Don't mistake a p-value of 0.06 for a p-value of 0.04 with bad luck.” “Practice safe statistics; randomize whenever possible.” References Seib KL, Donovan B, Jin F, et al. Meningococcal B Vaccine to Prevent Neisseria gonorrhoeae Infection. N Engl J Med. 2026; 395: 349-61. Petousis-Harris H, Paynter J, Morgan J, et al. Effectiveness of a group B outer membrane vesicle meningococcal vaccine against gonorrhoea in New Zealand: a retrospective case-control study. The Lancet. 2017; 390: 1603–10. Wang, B., Mohammed, H., Andraweera, P., McMillan, M., Marshall, H., 2024. Vaccine effectiveness and impact of meningococcal vaccines against gonococcal infections: A systematic review and meta-analysis. Journal of Infection. 2024; 89: 106225. Molina JM, Bercot B, Assoumou, L, et al. Doxycycline prophylaxis and meningococcal group B vaccine to prevent bacterial sexually transmitted infections in France (ANRS 174 DOXYVAC): a multicentre, open-label, randomised trial with a 2 × 2 factorial design. The Lancet Infectious Diseases. 2024; 24: 1093–1104. Thng C, Eskandari S, Jin F, et al. Efficacy of the meningococcal vaccine against Neisseria gonorrhoeae: a randomised clinical trial (MenGO). npj Vaccines. 2026. Still Not Significant | Probable Error: List of funny disguises scientists use to describe p>.05 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 (00:15) - - Introduction (00:51) - - The Claim: Can a Meningitis Vaccine Prevent Gonorrhea? (04:10) - - The Biology Behind the Idea (10:37) - - How Observational Studies Made the Link (14:44) - - Earlier Clinical Trials: MENGO and DOXYVAC (17:30) - - Flirting with Significance: A Statistical Rant (22:40) - - GoGoVax: Study Design and Analysis (33:31) - - The Shocking Null Result (35:38) - - Why These Results Don't Generalize (37:42) - - Rating the Claim

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  • #36
    July 27 · 44 min

    Hot Tubbing: Can soaking in hot water help you run faster?

    Can you improve your endurance by sitting in a hot tub instead of doing another hard workout? We dig into a study that put elite runners through five weeks of what we affectionately call “endurance hot tubbing” to see whether heat alone could trigger the same cardiovascular adaptations as altitude training. Along the way, we explore crossover study designs, multiple testing, why significant physiological changes don’t necessarily translate into better athletic performance, and how an exploratory regression analysis ended up answering the wrong scientific question. We also discover that serious hot tub research involves surprisingly little relaxation, debate whether bubbles should count as an experimental condition, and learn why distance runners pay good money to sleep in tents with some of the oxygen sucked out. Statistical topics best subsets regression causal inference crossover studies exploratory analyses mediation / mechanisms model specification multiple testing peer review sample size (00:15) - Introduction (00:44) - Can Hot Tubs Replace Exercise? (01:27) - Crazy Runners and Crazy Training (05:44) - The Amazon Delivery System Metaphor (12:12) - From Heat Training to Hot Tubs (13:33) - The Study: 10 Runners in Hot Water (22:31) - Results and the VO2 Max Question (29:26) - Statistical Sleuthing: Best Subsets Regression (36:50) - The Peer Review Problem (41:39) - Rating the Claim Methodologic Morals “A model can be very good at answering the wrong question.” “The label ‘peer reviewed’ does not guarantee ‘carefully reviewed’.” References Jenkins EJ, Killick JA, Zerilli O, et al. Long-term passive heat acclimation enhances maximal oxygen consumption via haematological and cardiac adaptation in endurance runners. J Physiol. Published online November 20, 2025. doi:10.1113/JP289874 Stembridge M, Jenkins E. Marathon training: Why hot baths might help you run faster. The Conversation. Published March 16, 2026. doi:10.64628/AB.6vknm9esh 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 (00:15) - Introduction (00:44) - Can Hot Tubs Replace Exercise? (01:27) - Crazy Runners and Crazy Training (05:44) - The Amazon Delivery System Metaphor (12:12) - From Heat Training to Hot Tubs (13:33) - The Study: 10 Runners in Hot Water (22:31) - Results and the VO2 Max Question (29:26) - Statistical Sleuthing: Best Subsets Regression (36:50) - The Peer Review Problem (41:39) - Rating the Claim

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  • #35
    July 13 · 47 min

    Exercise Snacks: Can just four minutes of exercise a day improve blood sugar?

    Could four one-minute bursts of exercise really improve blood sugar? We try “exercise snacks” ourselves before taking a close look at the clinical trial that inspired headlines. We explain why the study’s main result wasn’t statistically significant, how 34 secondary outcomes complicated the story, and what pre-registration can reveal about a study after it’s published. Along the way, we compare notes on our own exercise-snacking adventures, debate continuous glucose monitors, and ask how much evidence a single study should generate before it becomes health news. Statistical topics Crossover design Multiple testing Pre-registration Primary vs secondary outcomes Randomized controlled trial Research transparency Methodologic Morals “It's good relationship advice to be transparent. It's also good research advice.” “If the primary outcome is not significant, say it up top.” References Babir FJ, Marcotte-Chénard A, Sandilands RE, et al. Exercise snacks performed in real-world settings reduce postprandial hyperglycaemia and glycaemic variability in individuals living with type 2 diabetes: a randomised crossover study. Diabetologia. 2026;69(8):2200-2211. doi:10.1007/s00125-026-06741-2 https://www.washingtonpost.com/wellness/2026/05/28/4-minutes-exercise-day-could-help-control-blood-sugar/ clinicaltrials.gov pre-registration with changes: https://clinicaltrials.gov/study/NCT06382246?term=NCT06382246&rank=1&tab=history&a=1&b=2#version-content-panel 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 (04:06) - The claim: four minutes a day (07:05) - Our own N of 1 experiments (13:24) - The study (21:54) - Primary outcome: complete miss (25:16) - Secondary outcomes to the rescue? (35:02) - Statistical sleuthing and transparency (44:23) - Rating the claim

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  • #34
    June 29 · 57 min

    Cancer Blood Tests Part 2: The clinical trial

    How do you decide whether a clinical trial “worked”? In Part 2 of our Galleri series, we examine the landmark randomized trial of a blood test designed to detect more than 50 cancers. We explore why different outcome measures led to dramatically different headlines, discuss primary versus secondary outcomes, pre-registration, hierarchical testing, and post hoc analyses, and explain why mortality remains the outcome everyone is waiting for. Along the way, we uncover a statistical mystery involving dozens of missing cancers and discover how a little arithmetic can sometimes reveal more than a press release. Statistical topics cancer screening exploratory analyses hierarchical testing missing data multiple testing outcome measures post hoc analyses pre-registration primary and secondary outcomes randomized clinical trials screening tests Methodologic Morals “When the simple numbers don't add up, pay attention. The arithmetic may be trying to tell you something.” “The first question should not be, did it work? It should be, what counts as success?” References Giridhar KV, et al. Safety and performance results from PATHFINDER 2, a registrational study of a multi-cancer early detection test in an intended-use population. Presented at the 2026 American Society of Clinical Oncology (ASCO) Annual Meeting. May 2026. Hubbell E, Clarke CA, Aravanis AM, Berg CD. Modeled Reductions in Late-stage Cancer with a Multi-Cancer Early Detection Test. Cancer Epidemiol Biomarkers Prev. 2021;30(3):460-468. doi:10.1158/1055-9965.EPI-20-1134 Neal RD, Johnson P, Clarke CA, et al. Cell-Free DNA-Based Multi-Cancer Early Detection Test in an Asymptomatic Screening Population (NHS-Galleri): Design of a Pragmatic, Prospective Randomised Controlled Trial. Cancers (Basel). 2022;14(19):4818. Published 2022 Oct 1. doi:10.3390/cancers14194818 ASCO slides: https://grail.com/wp-content/uploads/2026/05/Swanton_ASCO-2026_NHS-Galleri_FINAL-Slides-05.26.2026.pdf UK registry protocol: https://www.isrctn.com/ISRCTN91431511 Clinicaltrials.gov protocol: https://clinicaltrials.gov/study/NCT05611632 Common biases in cancer screening studies Cancer screening studies are subject to several well-known biases that can make a screening test appear more effective than it actually is. Three of the most important are: Lead-time bias: Screening advances the time of diagnosis, making survival from diagnosis appear longer even if the patient's lifespan is unchanged. For example, if a screening test detects a Stage II cancer at age 60 that otherwise would have been diagnosed because of symptoms at age 62, but the patient dies at age 68 regardless, survival from diagnosis appears to increase from 6 years to 8 years even though the patient did not live any longer. Length bias: Screening preferentially detects slower-growing, less aggressive cancers because they remain detectable for longer than fast-growing cancers. For example, a slow-growing cancer that remains in Stage I for 5 years is much more likely to be found by screening than an aggressive cancer that progresses to symptoms within months. This can make screened patients appear to have better survival simply because screening preferentially found the less aggressive cancers. Overdiagnosis: Screening detects cancers that would never have caused symptoms or death during a person's lifetime, leading to unnecessary diagnosis and treatment. For example, a screening test may detect a very slow-growing prostate or thyroid cancer in an older adult that would never have become clinically important if it had remained undiscovered. 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 (03:39) - The Claim: Not Ready for Primetime (03:58) - Trial Design: 142,000 Participants (07:50) - The Primary Outcome Problem (20:29) - The Primary Endpoint: Complete Miss (22:14) - Three Arguments for the Defense (28:29) - - Statistical Sleuthing: Missing Cancers (41:14) - - The Stage Shift Argument (50:30) - - Rating the Claim

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  • #33
    June 15 · 43 min

    Cancer Blood Tests: Are they ready for primetime? Part 1

    Can a single tube of blood really detect dozens of cancers before symptoms appear? We dive into the science behind Galleri, a blood test that claims to detect more than 50 types of cancer from a simple blood draw. Recent headlines about the test ranged from “breakthrough” to “bust” after the release of results from a massive randomized clinical trial. In this Part 1 episode, we explore cell-free DNA, DNA methylation, machine learning, sensitivity, specificity, and positive predictive value. Along the way, we revisit the prenatal screening revolution, ask why detecting cancer earlier doesn’t always help patients, and learn how escaped DNA convicts end up swimming in a giant molecular pool party. And for the first time ever, Normal Curves ends on a cliffhanger: we’ll save the controversial results of that landmark trial for Part 2. Statistical topics cancer screening case-control studies counterfactuals machine learning negative predictive value overdiagnosis positive predictive value randomized clinical trials screening tests sensitivity and specificity validation References Bianchi DW, Chudova D, Sehnert AJ, et al. Noninvasive prenatal testing and incidental detection of occult maternal malignancies. JAMA. 2015; 314:162-9. Liu MC, Oxnard GR, Klein EA, et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann Oncol. 2020. 31:745-59. Schrag D, Beer T, McDonnell C et al. Blood-based tests for multicancer early detection (PATHFINDER): a prospective cohort study. The Lancet. 402: 1251-60. Giridhar KV, et al. Safety and performance results from PATHFINDER 2, a registrational study of a multi-cancer early detection test in an intended-use population. Presented at the 2026 American Society of Clinical Oncology (ASCO) Annual Meeting. May 2026. Statistic discussed in the episode PATHFINDER 2 investigators reported that adding Galleri to routine screening increased the number of screen-detected cancers by 6.5-fold. This figure compares 31 cancers detected through USPSTF-recommended screening (for breast, cervical, lung, and colon) with 204 cancers detected when Galleri was added, counting the same 31 conventional-screening cancers in both totals. Thus, describing the increase as 6.5-fold is misleading, since the combination of Galleri plus conventional screening is, by definition, guaranteed to detect at least as many cancers as conventional screening alone. Moreover, everyone in the study received Galleri, whereas conventional screening depended on which tests participants happened to be due for and completed during the study period. The comparison therefore does not involve two equally applied screening strategies. 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) - - Introduction (00:44) - - The Holy Grail of Cancer Testing (04:31) - - Headlines: Same Data, Opposite Stories (07:38) - - How Cell-Free DNA Works (13:54) - - DNA Methylation: GRAIL's Fingerprint (15:19) - - The Origin Story (22:18) - - The Pathfinder Studies (35:01) - - The Paradox: Why Earlier Detection Doesn't Always Help (40:32) - - The Cliffhanger

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  • #32
    June 1 · 53 min

    Odds Ratios: Do most people get them wrong?

    Odds ratios show up everywhere in medical research—but do readers, journalists, and even researchers always know what they mean? In this episode, we tackle one of the most common statistical misunderstandings in science: treating odds ratios like risk ratios. Along the way, we explore puppy photos, fish photos, first-date hookups, sugary drinks, cardiac care, and a listener challenge that started with an informal study of five medical residents and a box of chocolate truffles. We explain why logistic regression produces odds ratios, when odds ratios can wildly exaggerate effects, and why some famous headlines turned out to be much less dramatic than they sounded. Statistical topics binary outcomes case-control studies logistic regression odds ratios risk ratios odds vs risk Methodological morals “Just because logistic regression gives you an odds ratio does not mean you have to report it.” “A lot of bad science communication starts long before the journalist even enters the story.” References Bleich SN, Herring BJ, Flagg DD, et al. Reduction in purchases of sugar-sweetened beverages among low-income Black adolescents after exposure to caloric information. Am J Public Health. 2012;102:329–35. Sainani KL. How Statistics Can Mislead. Am J Public Health. 2012. 2012;102:e3–e4. Bleich SN, Herring BJ, Flagg DD, et al. Bleich et al. respond. Am J Public Health. 2012;102:e4. Press video: https://www.youtube.com/watch?v=IFyrqbf1XWs Sainani KL, Schmajuk G, Liu V. A Caution on Interpreting Odds Ratios. Sleep. 2009;32:976. Schulman KA, Berlin JA, Harless W, et al. The Effect of Race and Sex on Physicians' Recommendations for Cardiac Catheterization. NEJM. 1999;340:618–26. Schwartz LM, Woloshin S, Welch HG. Misunderstandings about the Effects of Race and Sex on Physicians' Referrals for Cardiac Catheterization. NEJM. 1999;341:279–83. Associated Press. Study Finds Bias in Doctors' Care of Women and Blacks. The New York Times. February 25, 1999. Knol MJ, Duijnhoven RG, Grobbee DE, et al. Potential Misinterpretation of Treatment Effects Due to Use of Odds Ratios and Logistic Regression in Randomized Controlled Trials. PLoS ONE. 2011;6:e21248. More information on logistic regression and odds ratios: Sainani KL. Logistic Regression. PM&R. 2014;6:1157–62. Sainani KL. Understanding Odds Ratios. PM&R. 2011;3:263–67. Nuzzo RL. Communicating measures of relative risk in plain English. PM&R. 2022;14:283-287. When outcomes are common, odds ratios can exaggerate effect sizes. Alternatives include: Presenting raw percentages (absolute risks) Presenting adjusted percentages from logistic regression (these may be calculated by plugging in means for the covariates) Converting odds ratios to risk ratios Reporting risk ratios directly when appropriate Converting Odds Ratios to Risk Ratios: Zhang J, Yu KF. What's the Relative Risk? A Method of Correcting the Odds Ratio in Cohort Studies of Common Outcomes. JAMA. 1998;280:1690–91. ClinCalc. Odds Ratio to Relative Risk Calculator. https://clincalc.com/stats/convertor.aspx RR = OR / [(1 − P0) + (P0 × OR)] Example: OR=0.51, baseline risk=93.3% RR = 0.51 / [(1 − 0.933) + (0.933 × 0.51)] = 0.51 / (0.067 + 0.476) = 0.51 / 0.543 = 0.94 Thus, an odds ratio of 0.51 corresponds to a risk ratio of 0.94 when the baseline risk is 93.3%. The corresponding unadjusted risk ratio is 86%/93.3%=0.92 Correction: In the episode, we stated that the adjusted risk ratio was 0.92. In fact, it is 0.94, as shown above. 0.92 is the unadjusted risk ratio. 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) - Introduction (02:54) - What Are Odds Ratios? (04:02) - Puppy Photos and First Dates (06:09) - Risk Ratio Explained (08:10) - Calculating Odds Ratios (11:09) - Fish Photos and Reversed Numbers (16:01) - Real-Life Example: Sugary Beverages (22:08) - How Logistic Regression Works (31:53) - The Video: Researchers Made the Mistake Themselves (36:30) - The Cardiac Catheterization Study (39:24) - The New York Times Printed a Correction (46:10) - Using OR and RR Interchangeably for Case Control (47:00) - Reye Syndrome and Aspirin (49:37) - Rating the Claim and Methodological Morals

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  • #31
    May 18 · 56 min

    Coffee and the Heart: Is caffeine a trigger for AFib?

    Does coffee trigger atrial fibrillation — or have doctors been warning people away from caffeine without strong evidence? We dig into two recent randomized clinical trials testing whether caffeinated coffee causes dangerous heart rhythm problems. Along the way, we talk about AFib, survival analysis, intention-to-treat versus as-treated analyses, and one surprisingly elaborate effort to catch clinical trial cheaters with receipts and geolocation tracking. We also explore how a pope may have fueled a European coffee resurgence, why plants make caffeine, and how a game show competition explains hazard ratios. Statistical topics adherence and compliance as-treated analysis confidence intervals Cox proportional hazards regression hazard ratios intention-to-treat analysis micro-randomization multiple testing PICOT pre-registration primary vs secondary outcomes randomized clinical trials sensitivity analyses SMART framework survival analysis Methodological morals “Never trust conventional wisdom until you see the randomized controlled trial.” “Trust your participants, but design the study so that they can be honest about their dishonesty.” References Harrington D, D'Agostino RB Sr, Gatsonis C, et al. New Guidelines for Statistical Reporting in the Journal. N Engl J Med. 2019;381(3):285-286. doi:10.1056/NEJMe1906559 Marcus GM, Rosenthal DG, Nah G, et al. Acute Effects of Coffee Consumption on Health among Ambulatory Adults. N Engl J Med. 2023;388(12):1092-1100. doi:10.1056/NEJMoa2204737 Wong CX, Cheung CC, Montenegro G, et al. Caffeinated Coffee Consumption or Abstinence to Reduce Atrial Fibrillation: The DECAF Randomized Clinical Trial. JAMA. 2026;335(4):317-325. doi:10.1001/jama.2025.21056 @MarcKatzMD’s short video The Pitt- atrial fibrillation cardioversion scene 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) - - Introduction (02:15) - - What is AFib? (04:36) - - Frisky Goats and Satan's Bitter Invention (10:44) - - How Caffeine Works (14:43) - - The CRAVE Trial (15:53) - - PICOT: Evaluating the Study Design (23:24) - - CRAVE Results (30:07) - - Catching the Coffee Cheaters (37:01) - - The DECAF Trial (41:30) - - Time-to-Event Outcomes (43:21) - - Hazard Ratios: Balance Beams Over Shark Tanks (47:06) - - DECAF Results: Team Coffee Wins (50:38) - - Why Would Coffee Be Protective? (53:57) - - Rating the Claim

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  • #30
    May 4 · 1 hr 4 min

    Sleep and Exercise: Does working out on too little sleep speed up aging?

    Can exercise actually be bad for you if you don’t get enough sleep? A widely shared claim says yes—that working out while sleep deprived may speed up aging. In this episode, we put that claim under the microscope. We examine the study behind it, unpack how sleep and aging were measured, and explore key statistical ideas like interaction effects and flexible models that can “dance” to the data. With the help of a $400,000 handbag and a man with seven boats, we also break down what it really takes to show that one variable changes the effect of another. What we find: some clear study bloopers, inconsistent modeling results, and interpretations that are flat-out wrong. Statistical topics Measurement error Model specification Piecewise linear regression Regression models Residual confounding Splines Statistical interactions Survey design Methodological morals “Before you believe something shocking, ask what had to go wrong to make it true.” “If slight modeling changes flip the story, there wasn't much story to begin with.” “Unethical Life Pro Tip: If you do not want your analysis critiqued, then just make it impossible to understand.” Kristin’s Biological Age Calculator References Original Viral Tweet: Ng D. "People who slept under 6 hours and exercised actually aged faster." X. March 9, 2026. Holmer B. Does exercise “age you faster” if you don’t sleep enough? Medium. March 16, 2026. You Y. Chen Y. Liu R., et al. Inverted U-shaped relationship between sleep duration and phenotypic age in US adults: a population-based study. Sci Rep. 2024;14:6247. Levine ME, Lu AT, Quach A, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging. 2018;10:573-591. 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) - Introduction (04:05) - What is NHANES? (06:38) - The Sleep Duration Results (12:50) - The 2015 Sleep Mystery (17:10) - Measuring Biological Aging (21:35) - The Penalized Cox Regression (28:16) - Sleep and Aging Results (30:03) - Cubic Splines and Dancing (36:49) - Adding Exercise to the Mix (40:57) - Boats, Handbags, and Interaction Effects (48:20) - The Cubic Spline Exercise Analysis (51:21) - The Opposite Result (55:54) - Academic Writing Gone Wrong (58:27) - The Writing Makeover (01:01:12) - Rating the Claim with Gatorinis

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  • #29
    April 20 · 1 hr 6 min

    Sex Recession: Are young people really having less sex?

    Are young people really having less sex? Headlines about a “sex recession” suggest a dramatic decline—but what do the data actually show? In this episode, we trace that claim back to the research behind it—and find a story that’s far more nuanced than the headlines suggest. We examine large national surveys, including the General Social Survey and the National Survey of Sexual Health and Behavior, and uncover how small analytical choices can completely change the story. Along the way, we tackle ordinal versus quantitative data, why averages can mislead, how logistic regression reframes the question, and what happens when researchers try to time-travel with statistics. Plus: the surprising role of extreme values, why “eight fewer sexual encounters per year” may not mean what you think, and whether young men and women are really following the same trends. Statistical topics Average vs distribution Binary variables Effect size vs statistical significance Logistic regression Measurement / operationalization Ordinal variables Outliers / extreme values Self-reported datagoog Social desirability bias Variable coding / transformation Methodological morals “You shouldn't use data from people in their 80s to guess what they were doing in their 20s unless your data come with a time machine.” “When extreme values drive the average, the average stops describing most people.” References Julian K. Why are young people having so little sex? The Atlantic. December 2018. Accessed April 19, 2026. https://www.theatlantic.com/magazine/archive/2018/12/the-sex-recession/573949/ Skwarecki B. Nearly half of Gen Z adults have never had sex: report. Newsweek. January 7, 2025. Accessed April 19, 2026. https://www.newsweek.com/nearly-half-of-gen-z-adults-have-never-had-sexreport-11052178 Virginity survey. DatingAdvice.com. Accessed April 19, 2026. https://www.datingadvice.com/studies/virginity-survey Twenge JM, Sherman RA, Wells BE. Declines in sexual frequency among American adults, 1989-2014. Arch Sex Behav. 2017;46(8):2389-2401. Ueda P, Mercer CH, Ghaznavi C, Herbenick D. Trends in frequency of sexual activity and number of sexual partners among adults aged 18 to 44 years in the US, 2000-2018. JAMA Netw Open. 2020;3(6):e203833. Herbenick D, Rosenberg M, Golzarri-Arroyo L, et al. Changes in penile-vaginal intercourse frequency and sexual repertoire from 2009 to 2018: findings from the National Survey of Sexual Health and Behavior. Arch Sex Behav. 2022;51(3):1419-1433. Wellings K, Palmer MJ, Machiyama K, Slaymaker E. Changes in, and factors associated with, frequency of sex in Britain: evidence from three National Surveys of Sexual Attitudes and Lifestyles (Natsal). BMJ. 2019;365:l1525. Published 2019 May 7. doi:10.1136/bmj.l1525 Burghardt J, Beutel ME, Hasenburg A, Schmutzer G, Brähler E. Declining Sexual Activity and Desire in Women: Findings from Representative German Surveys 2005 and 2016. Arch Sex Behav. 2020 Apr;49(3):919-925. doi: 10.1007/s10508-019-01525-9. Epub 2019 Dec 4. Erratum in: Arch Sex Behav. 2020 Apr;49(3):927. doi: 10.1007/s10508-019-01622-9. PMID: 31802290. Twenge JM. Possible Reasons US Adults Are Not Having Sex as Much as They Used To. JAMA Netw Open. 2020;3(6):e203889. Published 2020 Jun 1. doi:10.1001/jamanetworkopen.2020.3889 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) - Introduction (04:04) - Fact-Checking the Headlines (07:37) - The Twenge Study and the GSS (16:02) - The Hill-Shaped Trend (19:23) - The Ordinal Variable Problem (24:59) - The Married vs. Never-Married Paradox (27:42) - Time-Traveling to the 1920s (31:38) - The Ueda Study: A Better Approach (35:25) - The Two Classrooms (42:42) - What Counts as Sex? (48:52) - Historical Sex Terms (52:35) - The Sexual Repertoire Results (55:53) - Why Is This Happening? (01:02:12) - Rating the Claim

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  • #28
    April 6 · 1 hr 6 min

    Diagnostic Testing: Do the stats tell you what you need to know?

    Diagnostic testing: what do those statistics actually tell you? Sensitivity, specificity, positive predictive value . . . you’ve probably seen these terms before. Maybe you memorized them for a test. But do you actually know what they mean? In this episode, we take a closer look at how diagnostic tests are evaluated—and how they’re often misinterpreted. From a genetic test for cellulite to a blood test for autism, we explore how “statistically significant” findings can turn into tests that don’t actually help anyone. Along the way we meet the freckle gene, the wanderlust gene, and infidelity gene. Statistical topics Base Rate Bayes Rule Case-Control Study Matching Conditional Probability Sensitivity Specificity Positive Predictive Value Prevalence Negative Predictive Value False Positives and Negatives True Positives and Negatives Methodological morals “A biomarker paper is not the same thing as a biomarker test.” “If your sample doesn't match the real world, then for all of your positive predictive value needs, call on Bayes' theorem.” Detailed Show Notes with calculations References Emanuele E, Bertona M, Geroldi D. A multilocus candidate approach identifies ACE and HIF1A as susceptibility genes for cellulite. Journal of the European Academy of Dermatology and Venereology; 2010. 24: 930-35. https://genomelink.io/traits/cellulite https://www.genexdiagnostics.com/ Ebstein RP, Novick O, Umansky R, et al. Dopamine D4 receptor (D4DR) exon III polymorphism associated with the human personality trait of Novelty Seeking. Nat Genet. 1996;12:78-80. Kluger AN, Siegfried Z, Ebstein RP. A meta-analysis of the association between DRD4 polymorphism and novelty seeking. Mol Psychiatry. 2002;7:712-7. He Y, Martin N, Zhu G, Liu Y. Candidate genes for novelty-seeking: a meta-analysis of association studies of DRD4 exon III and COMT Val158Met. Psychiatr Genet. 2018 Dec;28(6):97-109. Smith AM, King JJ, West PR, et al. Amino Acid Dysregulation Metabotypes: Potential Biomarkers for Diagnosis and Individualized Treatment for Subtypes of Autism Spectrum Disorder. Biol Psychiatry. 2019;85:345-54. Sainani K, Goodman S. Lack of Diagnostic Utility of “Amino Acid Dysregulation Metabotypes.” Biol Psychiatry. 2018; 85: e41-e42. 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) - Introduction (02:24) - The Cellulite Test (06:41) - Understanding Sensitivity and Specificity (12:50) - Enter Positive Predictive Value (18:40) - Why Base Rates Matter (24:06) - More Ridiculous Tests (32:33) - The Wanderlust Gene Deep Dive (40:30) - The NeuroPoint Autism Test (51:37) - Trying to Set the Record Straight (01:00:42) - Personal Stories (01:03:57) - Wrap-up

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  • #27
    March 23 · 1 hr 9 min

    Epidurals: Are labor epidurals really linked to autism?

    Epidurals are widely used and widely trusted for pain relief during labor. So when a 2020 study reported that they might be linked to autism, it raised a troubling question: could a routine medical decision have long-term consequences? We follow that claim from headline to evidence—and watch what happens when other scientists take a closer look. We dig into the original study, a wave of replication studies from around the world, and a meta-analysis that tries to make sense of it all. Along the way, we unpack hazard ratios, Cox regression, inverse probability weighting, and sibling analyses—and why even sophisticated statistical adjustment can’t eliminate confounding. Plus: why bigger datasets don’t solve everything, what happens when results shrink after adjustment, and how a controversial study turned into a case study in science working as it should. Bonus: our first guest journalist interview! Statistical topics Confounding Cox regression Hazard ratios Inverse probability weighting (IPTW) Multivariable adjustment Observational studies Residual confounding Retrospective cohort studies Sibling analysis Statistical adjustment Statistical significance vs practical significance Survival analysis Methodological morals “Every time you adjust the model and the effect gets smaller, that's the universe whispering, maybe don't build a causal story out of this.” “Consistency across studies is gold.” “There's more to the story than the statistics.” References Dattaro, Laura. A questionable study linked autism to epidurals. Then what? Spectrum. April 18, 2023. Dattaro, Laura. How to find baby sharks. Nautilus. September 9. 2024. Laura Dattaro’s home page. Phil Kearney’s blog post about the SMART framework. Qiu C, Lin JC, Shi JM, et al. Association Between Epidural Analgesia During Labor and Risk of Autism Spectrum Disorders in Offspring. JAMA Pediatr. 2020;174:1168-1175. Joint Statement. Labor Epidurals Do Not Cause Autism; Safe for Mothers and Infants, say Anesthesiology, Obstetrics, and Pediatric Medical Societies. American Society of Anesthesiologists. October 12, 2020. Wall-Wieler E, Bateman BT, Hanlon-Dearman A, Roos LL, Butwick AJ. Association of Epidural Labor Analgesia With Offspring Risk of Autism Spectrum Disorders. JAMA Pediatr. 2021;175:698-705. Christakis DA. More on epidurals and autism. JAMA Pediatrics. 2021; 175: 705. Mikkelsen AP, Greiber IK, Scheller NM, Lidegaard Ø. Association of Labor Epidural Analgesia With Autism Spectrum Disorder in Children. JAMA. 2021;326:1170–1177. Hanley GE, Bickford C, Ip A, et al. Association of Epidural Analgesia During Labor and Delivery With Autism Spectrum Disorder in Offspring. JAMA. 2021;326:1178-1185. Hegvik TA, Klungsøyr K, Kuja-Halkola R, et al. Labor epidural analgesia and subsequent risk of offspring autism spectrum disorder and attention-deficit/hyperactivity disorder: a cross-national cohort study of 4.5 million individuals and their siblings. Am J Obstet Gynecol. 2023;228:233.e1-233.e12. Epub 2022 Aug 13. Hu X, Wang B, Chen J, Han D, Wu J. Association Between Epidural Labor Analgesia and Autism Spectrum Disorder in Offspring: A Systematic Review and Meta-Analysis. J Pain Res;17:227-240. 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 (01:40) - Why autism is hard to study (05:18) - The original 2020 study (11:38) - Results & hazard ratios (15:24) - Confounding & adjustment (26:32) - Criticism & plausibility (34:11) - Replications begin (44:38) - Converging evidence & meta-analysis (50:48) - What does it mean? (53:38) - Guest & wrap-up

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  • #26
    March 9 · 1 hr 4 min

    Daylight Saving Time: Does springing forward cause heart attacks?

    Every year we spring forward and lose an hour of sleep. But do we also lose a few heart cells? Some headlines claim that heart attacks spike by 24% after daylight saving time begins. In this episode we trace that number back to the research behind it—and what we find is more complicated than the headlines suggest. We examine a famous New England Journal of Medicine letter, a large international meta-analysis, and a massive modern U.S. registry study. Along the way we talk about incidence ratios, relative versus absolute risk, negative controls, and a haunting concept called harvesting. Plus: why bar charts are not for numerical data, why journalists love dramatic numbers, and how a bug collector helped invent daylight saving time. Statistical topics Incidence ratios / incidence rates Meta-analysis Negative controls Relative risk vs absolute risk Statistical vs practical significance Statistical Sleuthing Methodological morals “A bump in time isn’t always a bump in total.” “If you already know the story you want to tell, you can always find a number to tell it.” References Bourke, India. “An obsessed insect hunter: The creepy-crawly origins of daylight saving.” BBC Future, March 31, 2024. https://www.bbc.com/future/article/20240308-how-first-suggestions-of-daylight-savings-time-was-inspired-by-insects Fox-Skelly, Jasmin. “How Daylight Saving Time Affects Your Health.” BBC Future, October 25, 2025. https://www.bbc.com/future/article/20251024-how-daylight-saving-time-affects-our-health Hurst A, Morfeld P, Lewis P, Erren TC. Daylight Saving Time Transitions and Risk of Heart Attack. Dtsch Arztebl Int. 2024;121(15):490-496. doi:10.3238/arztebl.m2024.0078 Janszky I, Ljung R. Shifts to and from daylight saving time and incidence of myocardial infarction. N Engl J Med. 2008;359(18):1966-1968. doi:10.1056/NEJMc0807104 Jiddou MR, Pica M, Boura J, Qu L, Franklin BA. Incidence of myocardial infarction with shifts to and from daylight savings time. Am J Cardiol. 2013;111(5):631-635. doi:10.1016/j.amjcard.2012.11.010 Mellour, Richard. “The builder who changed how the world keeps time.” BBC Future, March 11, 2016. https://www.bbc.com/future/article/20160310-the-builder-who-changed-how-the-world-keeps-time Rymer JA, Li S, Chiswell K, et al. Daylight Savings Time and Acute Myocardial Infarction. JAMA Netw Open. 2025;8(9):e2530442. Published 2025 Sep 2. doi:10.1001/jamanetworkopen.2025.30442 https://graph2table.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) - Intro (05:03) - Strange history of daylight saving time (16:06) - Swedish NEJM study (19:14) - Incidence ratios explained (22:13) - What the Swedish study actually found (31:11) - Absolute vs relative risk (34:27) - Harvesting effect (40:10) - 2024 Meta-analysis (45:37) - Large modern US study (55:23) - Where the “24% increase” came from (59:16) - Wrap-up

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  • #25
    February 23 · 1 hr 5 min

    Marathon Performance: Does high-carb fueling work?

    How many carbs do you need to run your best marathon? Recent headlines suggest that 120 grams per hour is the magic number. But what’s the science behind that claim? To find out, we dug into the study fueling the hype — and were surprised by what we found. In this episode, we uncover numbers that mysteriously shift after peer review, figures that don’t match the text, and p-values that refuse to line up with their confidence intervals. Along the way, we swap bonking stories, revisit repeated-measures ANOVA, renew our antipathy for spreadsheets, and follow a trail of statistical termites to a surprisingly happy scientific ending. Statistical topics Article in press vs final publication Data management and workflow Multiple testing P-values and confidence intervals Repeated Measures ANOVA Statistical sleuthing Version control in research Within-person study design Methodological morals “Everyone makes statistical mistakes, not everyone fixes them.” “If the numbers aren't consistent, Excel is often part of the story.” “If a p-value doesn't survive the trip from text to figure, there's a problem.” Statistical Sleuthing Extended Notes References Ravikanti S, Silang KG, Martyn HJ, et al. 13C-labelled glucose–fructose show greater exogenous and whole-body CHO oxidation and lower O2 cost of running at 120 versus 60 and 90 g·h−1 in elite male marathoners. J Appl Physiol. 2025;139:1581–95. (final version) Article in Press of J Appl Physiol. 2025;139:1581–95. graph2table AI data extraction from figures. Use the discount code normalcurves20 for 20% off! Bob Kempainen gutting out the win at the 1996 U.S. Olympic Marathon Trials. N=7 is a Dinner Party LinkedIn Post WebPlotDigitizer Holmer B. The new high-carb study that’s rocking the running world. Marathon Handbook. Dec 5, 2025. Ivy JL, Miller W, Dover V, et al. Endurance improved by ingestion of a glucose polymer supplement. Med Sci Sports Exerc. 1983; 15:466–471. Coyle EF, Coggan AR, Hemmert MK, et al. Muscle glycogen utilization during prolonged strenuous exercise when fed carbohydrate. J Appl Physiol. 1986; 61:165–172. Coggan AR, Coyle EF. Reversal of fatigue during prolonged exercise by carbohydrate infusion or ingestion. J Appl Physiol. 1987; 63:2388–2395. Below PR, Mora-Rodríguez R, González-Alonso J, et al. Fluid and carbohydrate ingestion independently improve performance during 1 h of intense exercise. Med Sci Sports Exerc. 1995; 27:200–210. American College of Sports Medicine. Position stand: Nutrition and athletic performance. Med Sci Sports Exerc. 1996. Jeukendrup AE, Jentjens R. Oxidation of carbohydrate feedings during prolonged exercise: current thoughts, guidelines and directions for future research. Sports Med. 2000; 29:407–424. Currell K, Jeukendrup AE. Superior endurance performance with ingestion of multiple transportable carbohydrates. Med Sci Sports Exerc. 2008; 40:275–281. American Dietetic Association, Dietitians of Canada, American College of Sports Medicine, et al. Nutrition and athletic performance. Med Sci Sports Exerc. 2009; 41:709–731. Triplett D, Doyle JA, Rupp JC, et al. An isocaloric glucose–fructose beverage's effect on simulated 100-km cycling performance compared with a glucose-only beverage. Int J Sport Nutr Exerc Metab. 2010; 20:122–131. Stellingwerff T, Cox GR. Systematic review: carbohydrate supplementation on exercise performance or capacity of varying durations. Appl Physiol Nutr Metab. 2014; 39:998–1011. Thomas DT, Erdman KA, Burke LM. Position of the Academy of Nutrition and Dietetics, Dietitians of Canada, and the American College of Sports Medicine: Nutrition and athletic performance. Med Sci Sports Exerc. 2016. King AJ, O’Hara JP, Morrison DJ, et al. Carbohydrate dose influences liver and muscle glycogen oxidation and performance during prolonged exercise. Physiol Rep. 2018; 6:e13555. Urdampilleta A, Mielgo-Ayuso J, Martínez-Sanz JM, et al. Effects of 120 vs 90 g·h⁻¹ carbohydrate intake during a mountain marathon on neuromuscular function and high-intensity run capacity recovery. Nutrients. 2020; 12:2099. Podlogar T, Bescós R, Wallis GA, et al. Increased exogenous but unaltered endogenous carbohydrate oxidation with 120 vs 90 g·h⁻¹ carbohydrate ingestion during prolonged endurance exercise. Front Nutr. 2022; 9:936691. Smith JW, Pascoe DD, Passe DH, et al. Curvilinear dose-response relationship of carbohydrate (0–120 g·h⁻¹) and performance. Med Sci Sports Exerc. 2013; 45:336–341. Lukasiewicz C, Vandiver KJ, Albert ED, et al. Assessing exogenous carbohydrate intake needed to optimize human endurance performance across sex: insights from modeling runners pursuing a sub-2-hour marathon. J Appl Physiol. 2024. 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 ...

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  • #24
    February 9 · 1 hr

    Falling in Love: Do the 36 Questions Actually Work?

    Can a list of questions really make two strangers fall in love? In 2015, a viral New York Times Modern Love column claimed psychologists had discovered a formula for love: 36 increasingly personal questions, plus four minutes of eye contact. Millions of people tried it. There was even an app. But when we followed the citation trail back to the science, the story started to unravel. In this episode, we crack open the 1997 study behind the “36 Questions,” unearth a forgotten pilot study with a different (and sexier) protocol, and track down the real origin of the eye-gazing task. Along the way, we break down why control groups matter, why scale midpoints mislead, and why group averages aren’t people. We also try the questions on each other—purely for science, of course—and ask the nerdiest Valentine’s Day question of all: can a list of questions really make anyone fall in love? Statistical topics Control groups Correlated observations Group averages vs individual inference Pilot studies Reference distributions Scale interpretation Units of observation Methodological morals “Before you repeat a scientific claim, follow it back to the original study and read it carefully.” “You can slice the data into subgroups all you want, but that doesn't magically give you a control group. It gives you meaningless results.” Our version of the “40 Questions” app! References Aron, A., Aron, E.N., Melinat, E. and Vallone, R., 1991. Experimentally induced closeness, ego identity, and the opportunity to say no. In Conference of the International Network on Personal Relationships, Normal, IL. Aron, A., Melinat, E., Aron, E.N., Vallone, R.D. and Bator, R.J., 1997. The experimental generation of interpersonal closeness: A procedure and some preliminary findings. Personality and social psychology bulletin, 23(4), pp.363-377. Catron, Mandy L. To fall in love with anyone, do this. New York Times. January 11, 2015. https://www.nytimes.com/2015/01/11/style/modern-love-to-fall-in-love-with-anyone-do-this.html Catron, M.L., 2017. How to fall in love with anyone: a memoir in essays. Simon and Schuster. Jones, Daniel. The 36 Questions That Lead to Love. New York Times. January 9, 2015. https://www.nytimes.com/2015/01/09/style/no-37-big-wedding-or-small.html Kashdan, T.B. and Wenzel, A., 2005. A transactional approach to social anxiety and the genesis of interpersonal closeness: Self, partner, and social context. Behavior Therapy, 36(4), pp.335-346. Lee, Anna G. Long After ‘36 Questions,’ Finally Asking a Bigger One. New York Times. May 16, 2025. https://www.nytimes.com/2015/01/09/style/no-37-big-wedding-or-small.html Sprecher, S., 2021. Closeness and other affiliative outcomes generated from the Fast Friends procedure: A comparison with a small-talk task and unstructured self-disclosure and the moderating role of mode of communication. Journal of Social and Personal Relationships, 38(5), pp.1452-1471. Vacharkulksemsuk T, Fredrickson BL. Strangers in sync: Achieving embodied rapport through shared movements. J Exp Soc Psychol. 2012;48(1):399-402. doi:10.1016/j.jesp.2011.07.015 Mandy Len Catron’s TEDx talk: https://www.youtube.com/watch?v=v8Yo-PXN7UA Ivan Vendrov’s Twitter/X post about his exchange with Arthur Aron: https://x.com/IvanVendrov/status/1611809736823013377/photo/1 https://www.scientificamerican.com/podcast/episode/love-and-the-brain-part-1-the-36-questions-revisited/ Our version of the “40 Questions” app: https://www.normalcurves.com/questions-to-fall-in-love/ 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 (04:42) - Viral NYT Modern Love essay’s cultural influence (09:32) - Science behind the 36 questions (15:07) - The 1997 paper myth busting (18:52) - Sleuthing the pilot study (29:44) - What did the 1997 paper actually show (41:24) - Discussion section (49:58) - Did it replicate (56:47) - Wrap up

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  • January 26 · 57 min

    Bonus: Pheromones with commentary

    While we’re on a short break between seasons, we’re revisiting some of our favorite episodes from Season 1. This week, we’re re-releasing our debut episode on pheromones and sexy sweat, with some added commentary up front.. Sweaty t-shirt dating parties, sex pheromone dating sites, choosing your dating partner by sniffing them up — wacko fringe fads or evidence-based mating strategies? And what does your armpit stain have to do with your kids’ immune systems, or hormonal contraceptive pills, or divorce rates? In this episode, we reach back into the 1990s and revisit the scientific paper that started it all: The Sweaty T-Shirt Study. They bring a sharp eye and open mind, critically examining the study and following the line of research to today. Along the way, they encounter interesting statistical topics – including correlated observations, within-person study design, and bar chart blasphemy – with a short, surprising detour into Neanderthal sex. Statistical topics Bar charts Correlated observations Cherry-picking Data and methodological transparency Multiple testing Post-hoc analyses Unit of observation / unit of anaysis Within-person study design Methodological morals “Repeat after me: Bar charts are not for numerical data.” “Those who ignore dependencies in their data are destined for flawed conclusions.” References Nuzzo, R. Ah, Love at first whiff. Los Angeles Times. May 19, 2008. Papamarko, S. Pheromone parties attempt to match singles by scent. Yahoo!life. April 12, 2012. Sainani, K. Stone Age Gene Swap. Stanford Magazine. November/December 2011. Aldhous, P. Darling, You Smell Wonderfully Different. New Scientist. 6 May 1995. Wedekind C, Seebeck T, Bettens F, Paepke AJ. MHC-dependent mate preferences in humans. Proc Biol Sci. 1995; 260(1359):245-249. doi:10.1098/rspb.1995.0087 Hedrick P, Loeschcke V. MHC and mate selection in humans?. Trends Ecol Evol. 1996;11(1):24. doi:10.1016/0169-5347(96)80237-0 Wedekind C, Seebeck T. Reply from C. Wedekind and T. Seebeck. Trends Ecol Evol. 1996;11(1):24-25. doi:10.1016/0169-5347(96)81061-5 Wedekind C, Füri S. Body odour preferences in men and women: do they aim for specific MHC combinations or simply heterozygosity?. Proc Biol Sci. 1997;264(1387):1471-1479. doi:10.1098/rspb.1997.0204 Havlíček J, Winternitz J, Roberts SC. Major histocompatibility complex-associated odour preferences and human mate choice: near and far horizons. Philos Trans R Soc Lond B Biol Sci. 2020;375(1800):20190260. doi:10.1098/rstb.2019.0260 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) - Introduction (00:27) - Pheromone Dating Parties (00:57) - Pheromone Dating Sites and Genetic Matching (00:47) - The Science of HLA Genes and Mate Selection (00:08) - Breaking Down the Original Sweaty T-Shirt Study (00:08) - Study Design Flaws and Data Transparency Issues (00:31) - Statistical Flaws: Correlated Observations Explained (00:22) - Analyzing the Study's Questionable Results (00:18) - The Pill's Influence on Scent Preferences (00:26) - Overstated Conclusions and Wandering Discussions (00:09) - Media Reactions and the Study’s Public Impact (00:22) - Other Studies and their results (00:01) - Conclusion

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  • January 12 · 1 hr 8 min

    Bonus: Sugar Sag with Commentary

    While we’re on a short break between seasons, we’re revisiting some of our favorite episodes from Season 1. This week, we’re re-releasing our exploration of how your diet can affect your skin – now with added commentary! Wrinkles and sagging skin—just normal aging, or can you blame your sweet tooth? We dive into “sugar sag,” exploring how sugar, processed foods, and even your crispy breakfast toast might be making you look older than if you’d said no to chocolate cake and yes to broccoli. Along the way, we encounter statistical adjustment, training and test data sets, what we call “references to nowhere,” plus some cadavers and collagen. Ever heard of an AGE reader? Find out how this tool might offer a sneak peek at your date’s age—and maybe even a clue about his… um… “performance.” Statistical topics Confounding Correlation vs causation Measurement error / proxy variables Overfitting Plagiarism Proper citing practices References to nowhere Statistical adjustment Training and test sets Methodologic morals “When you plagiarize, you steal the errors too.” “Overdone statistical adjustment is like overdone photo filters–at a certain point it’s just laughable.” Citations Collagen turnover: Verzijl N, DeGroot J, Thorpe SR, et al.Effect of Collagen Turnover on the Accumulation of Advanced Glycation End Products. JBC. 2000;275:39027-31. Cadaver study: Hamlin CR, Kohn RR, Luschin JH. Apparent Accelerated Aging of Human Collagen in Diabetes Mellitus. Diabetes. 1975; 24: 902–904. AGE Reader Studies of AGEs and diabetes and health: Monnier VM, Cerami A. Nonenzymatic browning in vivo: possible process for aging of long-lived proteins. Science. 1981;211:491-3. Brownlee M, Vlassara H, Cerami A. Nonenzymatic glycosylation and the pathogenesis of diabetic complications. Ann Intern Med. 1984;101:527-37. Monnier VM, Vishwanath V, Frank KE, et al. Relation between Complications of Type I Diabetes Mellitus and Collagen-Linked Fluorescence. N Engl J Med. 1986;314:403-408. Monnier VM, Sell DR, Abdul-Karim FW, et al. Collagen browning and cross-linking are increased in chronic experimental hyperglycemia. Relevance to diabetes and aging. Diabetes. 1988;37:867-72. Monnier VM, Bautista O, Kenny D, et al. Skin collagen glycation, glycoxidation, and crosslinking are lower in subjects with long-term intensive versus conventional therapy of type 1 diabetes: relevance of glycated collagen products versus HbA1c as markers of diabetic complications. Diabetes 1999; 48: 870–80. Genuth S, Sun W, Cleary P, et al. Glycation and carboxymethyllysine levels in skin collagen predict the risk of future 10-year progression of diabetic retinopathy and nephropathy in the diabetes control and complications trial and epidemiology of diabetes interventions and complications participants with type 1 diabetes. Diabetes. 2005;54:3103-11. van Waateringe RP, Slagter SN, van Beek AP, et al. Skin autofluorescence, a non-invasive biomarker for advanced glycation end products, is associated with the metabolic syndrome and its individual components. Diabetol Metab Syndr. 2017;9:42. Kouidrat Y, Zaitouni A, Amad A, et al. Skin autofluorescence (a marker for advanced glycation end products) and erectile dysfunction in diabetes. J Diabetes Complications. 2017;3:108-113. Fujita N, Ishida M, Iwane T, et al. Association between Advanced Glycation End-Products, Carotenoids, and Severe Erectile Dysfunction. World J Mens Health. 2023;41:701-11. Uruska A, Gandecka A, Araszkiewicz A, et al. Accumulation of advanced glycation end products in the skin is accelerated in relation to insulin resistance in people with Type 1 diabetes mellitus. Diabet Med. 2019;36:620-625. Boersma HE, Smit AJ, Paterson AD, et al. Skin autofluorescence and cause-specific mortality in a population-based cohort. Sci Rep 2024;14:19967. Review article with conflicts of interest: Draelos ZD. Sugar Sag: What Is Skin Glycation and How Do You Combat It? J Drugs Dermatol. 2024; 23:s5-10. Clinical study on AGE interrupter cream: Draelos ZD, Yatskayer M, Raab S, Oresajo C. An evaluation of the effect of a topical product containing C-xyloside and blueberry extract on the appearance of type II diabetic skin. J Cosmet Dermatol. 2009;8:147-51. Our citation trail: 2023 review article: Zgutka K, Tkacz M, Tomasiak, et al. A Role for Advanced Glycation End Products in Molecular Ageing. Int J Mol Sci. 2023; 24: 9881. Sentence: “Interestingly, strict control of blood sugar for 4 months reduced the production of glycosylated collagen by 25%, and low-sugar food prepared by boiling could also reduce the production of AGEs [152].” Reference 152 is a review article: Cao C, Xiao Z, Wu Y, et al. Diet and Skin Aging-From the Perspective of Food Nutrition. Nutrients. 2020;12:870. Sentence: “However, strict control of blood sugar for four months can reduce the production of glycosylated collagen by 25%, and low-sugar food prepared by boiling can also reduce the production of AGEs [93–95].” Reference 93 is a review article: Nguyen HP, Katta R. Sugar sag: Glycation and the role of diet in aging skin. Skin Ther Lett. 2015; 20: 1–5. Sentence: “Tight glycemic control over a 4-month period can result in a reduction of glycated collagen formation by 25%.37,38” Reference 94 and 38 is a review article: Draelos ZD. Aging skin: the role of diet: facts and controversies. Clin Dermatol. 2013;31:701-6. Sentence: “Tighter glycemic control can reduce glycated collagen by 25% in 4 months.” No citation given. ...

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  • Dec 29, 2025 · 1 hr 23 min

    Bonus: Vitamin D Part 1 with commentary

    While we’re on a short break between seasons, we’re revisiting some of our favorite episodes from Season 1. This week, we’re re-releasing our deep dive into vitamin D and the origins of the so-called deficiency epidemic, with added commentary. Is America really facing an epidemic of vitamin D deficiency? While this claim is widely believed, the story behind it is packed with twists, turns, and some pesky statistical cockroaches. In this episode, we’ll dive into a study on Hawaiian surfers, expose how shifting goalposts can create an epidemic, tackle dueling medical guidelines, and flex our statistical sleuthing skills. By the end, you might wonder if the real deficiency lies in the data. Statistical topics conflicts of interest cut points and thresholds dichotomization incentives in science measurement and classification normal distribution researcher biases standard deviation statistical sleuthing Methodologic morals “Arbitrary thresholds make for arbitrary diseases.” “Statistical errors are like cockroaches: Where there’s one, there’s many.” Note that all blood vitamin D levels discussed in the podcast are 25-hydroxyvitamin D levels given in units of ng/ml. To convert from ng/ml to nmol/L, use the formula: nmol/L=2.5*ng/ml. For example, a vitamin D level of 30 ng/mL corresponds to 75 nmol/L. Citations Dr. Rhonda Patrick: Micronutrients for Health & Longevity. Huberman Lab Podcast. May 1, 2022 Noh CK, Lee MJ, Kim BK, et al. A Case of Nutritional Osteomalacia in Young Adult Male. J Bone Metab. 2013; 20:51-55. Binkley N, Novotny R, Krueger D, et al. Low vitamin D status despite abundant sun exposure. J Clin Endocrinol Metab. 2007;92:2130-5. Malabanan A, Veronikis IE, Holick MF. Redefining Vitamin D Insufficiency. Lancet. 1998;351:805-6. Dawson-Hughes B, Heaney RP, Holick MF, et al. Estimates of optimal vitamin D status. Osteoporos Int. 2005;16:713-6. Holick MF. Vitamin D deficiency. N Engl J Med. 2007;357:266-81. Cui A, Xiao P, Ma Y, et al. Prevalence, trend, and predictor analyses of vitamin D deficiency in the US population, 2001-2018. Front Nutr. 2022;9:965376. Ross AC, Manson JE, Abrams SA, et al. The 2011 report on dietary reference intakes for calcium and vitamin D from the Institute of Medicine: what clinicians need to know. J Clin Endocrinol Metab. 2011;96:53-8. Holick MF, Binkley NC, Bischoff-Ferrari HA, et al. Evaluation, Treatment, and Prevention of Vitamin D Deficiency: an Endocrine Society Clinical Practice Guideline. J Clin Endocrinol Metab. 2011;96:1911-30. Manson JE, Brannon PM, Rosen CJ, et al. Vitamin D deficiency-is there really a pandemic. N Engl J Med. 2016;375:1817-20. Conti G, Chirico V, Lacquaniti A, et al. Vitamin D intoxication in two brothers: be careful with dietary supplements. J Pediatr Endocrinol Metab. 2014;27:763-7. Holick, Michael, et al. The UV Advantage. Ibooks, 2004. Holick, Michael F. The Vitamin D Solution: A 3-Step Strategy to Cure Our Most Common Health Problems. Penguin Publishing Group, 2011. Szabo, Liz. Vitamin D, the Sunshine Supplement, Has Shadowy Money Behind It. The New York Times. August 18, 2018. Lee JM, Smith JR, Philipp BL, Chen TC, Mathieu J, Holick MF. Vitamin D deficiency in a healthy group of mothers and newborn infants. Clin Pediatr. 2007;46:42-4. Holick MF. Vitamin D deficiency: what a pain it is. Mayo Clin Proc. 2003;78:1457-9. Passeri G, Pini G, Troiano L, et al. Low Vitamin D Status, High Bone Turnover, and Bone Fractures in Centenarians. J Clin Endocrinol Metab. 2003;88:5109-15. Armstrong, David. The Child Abuse Contrarian. ProPublica. September 16, 2018. Irwig MS, Kyinn M, Shefa MC. Financial Conflicts of Interest Among Authors of Endocrine Society Clinical Practice Guidelines. J Clin Endocrinol Metab. 2018;103:4333-38. Demay MB, Pittas AG, Bikle DD, et al. Vitamin D for the Prevention of Disease: An Endocrine Society Clinical Practice Guideline. J Clin Endocrinol Metab. 2024;109:1907-47. McCartney CR, McDonnell ME, Corrigan MD, et al. Vitamin D Insufficiency and Epistemic Humility: An Endocrine Society Guideline Communication. J Clin Endocrinol Metab. 2024; 109:1948–54. See our detailed notes here 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...

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  • #23
    Dec 15, 2025 · 46 min

    The Batman Effect: Do weird surprises make people nicer?

    Description Nobody expects Batman—but when he shows up in a crowded subway car, are people suddenly more likely to help a passenger in need? This week on Normal Curves, we unpack a recent quasi-experimental field study involving a caped superhero costume, a prosthetic pregnancy belly, and some puzzled Italian commuters. Along the way, we demystify three common ways of describing effects for binary outcomes—risk differences, risk ratios, and odds ratios—and explain what they actually mean in plain language. We also do some statistical sleuthing, uncover a major problem hiding in the paper’s numbers, and debate what really counts as an effective Batman outfit. Statistical topics absolute vs relative effects binary outcomes coding errors data errors and quality control effect size interpretation field experiments odds odds ratios percentage differences quasi-experimental studies risk differences risk ratios statistical sleuthing Methodological morals “We love an uncluttered paper, but when it's missing the basics, it's like an empty fridge. Clean, yes, but dinner is not happening.” “Before you make a fancy model, make sure the numbers in the table in the text match.” References Pagnini F, Grosso F, Cavalera C, et al. Unexpected events and prosocial behavior: the Batman effect. Npj Ment Health Res. 2025;4(1):57. Published 2025 Nov 3. doi:10.1038/s44184-025-00171-5 PubPeer. Comments on “Unexpected events and prosocial behavior: the Batman effect.” Accessed December 2025. Sainani KL. Understanding odds ratios. PM R. 2011;3(3):263-267. doi:10.1016/j.pmrj.2011.01.009 Nuzzo RL. Communicating measures of relative risk in plain English. PM R. 2022;14(2):283-287. doi:10.1002/pmrj.12761 Sainani KL. How statistics can mislead. Am J Public Health. 2012;102:e3-4. 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 (03:42) - Why would Batman make people nicer? (07:33) - How they ran the experiment (17:06) - Did Batman save the day? Different ways to answer that (22:16) - What are odds and odds ratios? (29:16) - Where people get it wrong (34:08) - The plot twist: big numerical errors (40:36) - Did men or women give up their seat more often? (43:05) - Wrap-up and methodological morals

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  • #22
    Dec 1, 2025 · 1 hr 2 min

    Holiday Survival Guide Part 2: The survey study edition

    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? (19:25) - PICOT and PIVOT (25:36) - Memory problems (31:19) - Five outcomes and measurement problems therein (36:12) - SMART (40:31) - Multiple Testing Dude is having a great time (51:17) - How big is the effect? (57:47) - Wrap-up and Irish Coffee rating scale

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