Normal Curves: Sexy Science, Serious Statistics

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

(23:05) - How Logistic Regression Works

(32:50) - The Video: Researchers Made the Mistake Themselves

(37:27) - The Cardiac Catheterization Study

(40:21) - The New York Times Printed a Correction

(47:07) - Using OR and RR Interchangeably for Case Control

(47:57) - Reye Syndrome and Aspirin

(50:34) - Rating the Claim and Methodological Morals

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

(24:21) - - CRAVE Results

(31:04) - - Catching the Coffee Cheaters

(37:58) - - The DECAF Trial

(42:49) - - Time-to-Event Outcomes

(44:40) - - Hazard Ratios: Balance Beams Over Shark Tanks

(48:25) - - DECAF Results: Team Coffee Wins

(51:57) - - Why Would Coffee Be Protective?

(55:16) - - Rating the Claim

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

(22:32) - The Penalized Cox Regression

(29:13) - Sleep and Aging Results

(31:00) - Cubic Splines and Dancing

(38:08) - Adding Exercise to the Mix

(42:16) - Boats, Handbags, and Interaction Effects

(49:39) - The Cubic Spline Exercise Analysis

(52:40) - The Opposite Result

(57:13) - Academic Writing Gone Wrong

(59:46) - The Writing Makeover

(01:02:31) - Rating the Claim with Gatorinis

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

(28:39) - Time-Traveling to the 1920s

(32:35) - The Ueda Study: A Better Approach

(36:22) - The Two Classrooms

(43:39) - What Counts as Sex?

(50:49) - Historical Sex Terms

(54:32) - The Sexual Repertoire Results

(57:50) - Why Is This Happening?

(01:04:09) - Rating the Claim

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

(33:30) - The Wanderlust Gene Deep Dive

(41:27) - The NeuroPoint Autism Test

(53:34) - Trying to Set the Record Straight

(01:02:39) - Personal Stories

(01:05:54) - Wrap-up