Normal Curves: Sexy Science, Serious Statistics

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

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

(27:29) - Criticism & plausibility

(35:08) - Replications begin

(45:57) - Converging evidence & meta-analysis

(52:07) - What does it mean?

(54:57) - Guest & wrap-up

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

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 
...