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
• (19:49) - Sleuthing the pilot study
• (30:41) - What did the 1997 paper actually show
• (42:21) - Discussion section
• (51:55) - Did it replicate
• (58:44) - Wrap up

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
• (06:09) - Conclusion
• (06:16) - Breaking Down the Original Sweaty T-Shirt Study
• (06:16) - Study Design Flaws and Data Transparency Issues
• (06:17) - Media Reactions and the Study’s Public Impact
• (06:26) - The Pill's Influence on Scent Preferences
• (06:30) - Analyzing the Study's Questionable Results
• (06:30) - Other Studies and their results
• (06:34) - Overstated Conclusions and Wandering Discussions
• (06:35) - Pheromone Dating Parties
• (06:39) - Statistical Flaws: Correlated Observations Explained
• (06:55) - The Science of HLA Genes and Mate Selection
• (07:05) - Pheromone Dating Sites and Genetic Matching

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....

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...

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
• (17:50) - Did Batman save the day? Different ways to answer that
• (23:00) - What are odds and odds ratios?
• (30:00) - Where people get it wrong
• (34:52) - The plot twist: big numerical errors
• (41:20) - Did men or women give up their seat more often?
• (43:49) - Wrap-up and methodological morals

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

Statistical topics

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

Methodological morals

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

References

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

Kristin and Regina’s online courses: 

Demystifying Data: A Modern Approach to Statistical Understanding  

Clinical Trials: Design, Strategy, and Analysis 

Medical Statistics Certificate Program  

Writing in the Sciences 

Epidemiology and Clinical Research Graduate Certificate Program 

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program 

Find us on:

Kristin -  LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

• (00:00) - Intro
• (04:36) - Did they have real research hypotheses?
• (10:29) - Observational or randomized experiment?
• (20:09) - PICOT and PIVOT
• (26:20) - Memory problems
• (32:03) - Five outcomes and measurement problems therein
• (36:56) - SMART
• (41:50) - Multiple Testing Dude is having a great time
• (52:36) - How big is the effect?
• (59:06) - Wrap-up and Irish Coffee rating scale

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

Statistical topics

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

Methodological morals

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

References

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

Kristin and Regina’s online courses: 

Demystifying Data: A Modern Approach to Statistical Understanding  

Clinical Trials: Design, Strategy, and Analysis 

Medical Statistics Certificate Program  

Writing in the Sciences 

Epidemiology and Clinical Research Graduate Certificate Program 

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program 

Find us on:

Kristin -  LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

• (00:00) - Intro
• (03:30) - Uncle Joe and the question of alcohol
• (07:20) - Randomized controlled trial
• (10:10) - PICOT mnemonic
• (15:43) - Just how drunk?
• (22:25) - Boring non-placeb
• (33:13) - Kristin’s SMART mnemonic
• (39:32) - How big of an effect?
• (50:46) - Two science journalists walk into a bar
• (57:00) - Martini scale and wrap-up

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

Statistical topics

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

Methodological morals

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

References

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

Detailed Show Notes Page

Kristin and Regina’s online courses: 

Demystifying Data: A Modern Approach to Statistical Understanding  

Clinical Trials: Design, Strategy, and Analysis 

Medical Statistics Certificate Program  

Writing in the Sciences 

Epidemiology and Clinical Research Graduate Certificate Program 

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program 

Find us on:

Kristin -  LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

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

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

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

Statistical topics

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

Methodological morals

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

References

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

Kristin and Regina’s online courses: 

Demystifying Data: A Modern Approach to Statistical Understanding  

Clinical Trials: Design, Strategy, and Analysis 

Medical Statistics Certificate Program  

Writing in the Sciences 

Epidemiology and Clinical Research Graduate Certificate Program 

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program 

Find us on:

Kristin -  LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

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

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

Statistical topics

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

Methodological morals

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

References

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

Kristin and Regina’s online courses: 

Demystifying Data: A Modern Approach to Statistical Understanding  

Clinical Trials: Design, Strategy, and Analysis 

Medical Statistics Certificate Program  

Writing in the Sciences 

Epidemiology and Clinical Research Graduate Certificate Program 

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program 

Find us on:

Kristin -  LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

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

P-values show up in almost every scientific paper, yet they’re one of the most misunderstood ideas in statistics. In this episode, we break from our usual journal-club format to unpack what a p-value really is, why researchers have fought about it for a century, and how that famous 0.05 cutoff became enshrined in science. Along the way, we share stories from our own papers—from a Nature feature that helped reshape the debate to a statistical sleuthing project that uncovered a faulty method in sports science. The result: a behind-the-scenes look at how one statistical tool has shaped the culture of science itself.

Statistical topics

• Bayesian statistics
• Confidence intervals 
• Effect size vs. statistical significance
• Fisher’s conception of p-values
• Frequentist perspective
• Magnitude-Based Inference (MBI)
• Multiple testing / multiple comparisons
• Neyman-Pearson hypothesis testing framework
• P-hacking
• Posterior probabilities
• Preregistration and registered reports
• Prior probabilities
• P-values
• Researcher degrees of freedom
• Significance thresholds (p < 0.05)
• Simulation-based inference
• Statistical power 
• Statistical significance
• Transparency in research 
• Type I error (false positive)
• Type II error (false negative)
• Winner’s Curse

Methodological morals

• “​​If p-values tell us the probability the null is true, then octopuses are psychic.”
• “Statistical tools don't fool us, blind faith in them does.”

References

• Nuzzo R. Scientific method: statistical errors. Nature. 2014 Feb 13;506(7487):150-2. doi: 10.1038/506150a. 
• Nuzzo, R., 2015. Scientists perturbed by loss of stat tools to sift research fudge from fact. Scientific American, pp.16-18.
• Nuzzo RL. The inverse fallacy and interpreting P values. PM&R. 2015 Mar;7(3):311-4. doi: 10.1016/j.pmrj.2015.02.011. Epub 2015 Feb 25. 
• Nuzzo, R., 2015. Probability wars. New Scientist, 225(3012), pp.38-41.
• Sainani KL. Putting P values in perspective. PM&R. 2009 Sep;1(9):873-7. doi: 10.1016/j.pmrj.2009.07.003.
• Sainani KL. Clinical versus statistical significance. PM&R. 2012 Jun;4(6):442-5. doi: 10.1016/j.pmrj.2012.04.014.
• McLaughlin MJ, Sainani KL. Bonferroni, Holm, and Hochberg corrections: fun names, serious changes to p values. PM&R. 2014 Jun;6(6):544-6. doi: 10.1016/j.pmrj.2014.04.006. Epub 2014 Apr 22. 
• Sainani KL. The Problem with "Magnitude-based Inference". Med Sci Sports Exerc. 2018 Oct;50(10):2166-2176. doi: 10.1249/MSS.0000000000001645. 
• Sainani KL, Lohse KR, Jones PR, Vickers A. Magnitude-based Inference is not Bayesian and is not a valid method of inference. Scand J Med Sci Sports. 2019 Sep;29(9):1428-1436. doi: 10.1111/sms.13491. 
• Lohse KR, Sainani KL, Taylor JA, Butson ML, Knight EJ, Vickers AJ. Systematic review of the use of "magnitude-based inference" in sports science and medicine. PLoS One. 2020 Jun 26;15(6):e0235318. doi: 10.1371/journal.pone.0235318. 
• Wasserstein, R.L. and Lazar, N.A., 2016. The ASA statement on p-values: context, process, and purpose. The American Statistician, 70(2), pp.129-133.

Kristin and Regina’s online courses: 

Demystifying Data: A Modern Approach to Statistical Understanding  

Clinical Trials: Design, Strategy, and Analysis 

Medical Statistics Certificate Program  

Writing in the Sciences 

Epidemiology and Clinical Research Graduate Certificate Program 

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program 

Find us on:

Kristin -  LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

• (00:00) - Intro & claim of the episode
• (01:00) - Why p-values matter in science
• (02:44) - What is a p-value? (ESP guessing game)
• (06:47) - Big vs. small p-values (psychic octopus example)
• (08:29) - Significance thresholds and the 0.05 rule
• (09:00) - Regina’s Nature paper on p-values
• (11:32) - Misconceptions about p-values
• (13:18) - Fisher vs. Neyman-Pearson (history & feud)
• (16:26) - Botox analogy and type I vs. type II errors
• (19:41) - Dating app analogies for false positives/negatives
• (22:02) - How the 0.05 cutoff got enshrined
• (24:40) - Misinterpretations: statistical vs. practical significance
• (26:16) - Effect size, sample size, and “statistically discernible”
• (26:45) - P-hacking and researcher degrees of freedom
• (29:46) - Transparency, preregistration, and open science
• (30:52) - The 0.05 cutoff trap (p = 0.049 vs 0.051)
• (31:18) - The biggest misinterpretation: what p-values actually mean
• (33:29) - Paul the psychic octopus (worked example)
• (35:59) - Why Bayesian statistics differ
• (39:49) - Why aren’t we all Bayesian? (probability wars)
• (41:05) - The ASA p-value statement (behind the scenes)
• (43:16) - Key principles from the ASA white paper
• (44:15) - Wrapping up Regina’s paper
• (45:33) - Kristin’s paper on sports science (MBI)
• (48:10) - What MBI is and how it spread
• (50:43) - How Kristin got pulled in (Christie Aschwanden & FiveThirtyEight)
• (54:05) - Critiques of MBI and “Bayesian monster” rebuttal
• (56:14) - Spreadsheet autopsies (Welsh & Knight)
• (58:05) - Cherry juice example (why MBI misleads)
• (01:00:22) - Rebuttals and smoke & mirrors from MBI advocates
• (01:02:55) - Winner’s Curse and small samples
• (01:03:38) - Twitter fights & “establishment statistician”
• (01:05:56) - Cult-like following & Matrix red pill analogy
• (01:08:06) - Wrap-up