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