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

Exercise has long been hailed as cancer-fighting magic, but is there hard evidence behind the hype? In this episode, we tackle the CHALLENGE trial, a large phase III study of colon cancer patients that tested whether prescribed exercise could improve cancer-free survival. We translate clinical jargon into plain English, show why ratio statistics make splashy headlines while absolute differences tell the real story, and take a detour into why statisticians think survival analysis is downright sexy. And we even bring in a classic reality show to make sense of the numbers.

Statistical topics

• Data and Safety Monitoring Board (DSMB)
• Hazard ratios
• Intention-to-treat analysis
• Interim analyses
• Kaplan-Meier curves
• Phase III trials
• Randomized clinical trial
• Rates and rate ratios
• Relative vs absolute differences
• Stratified randomization with minimization
• Survival analysis
• Time-to-event variables

Methodological morals

• “Ratio statistics sell headlines. Absolute differences sell truth.”
• “Survival analysis is this sexy stats tool that makes every moment and every Cox count.”

References

• Courneya KS, Vardy JL, O'Callaghan CJ, et al. Structured Exercise after Adjuvant Chemotherapy for Colon Cancer. NEJM. 2025;393:13-25. 
• Rabin RC. Are Marathons and Extreme Running Linked to Colon Cancer? The New York Times. Aug 19, 2025.
• Sainani KL. Introduction to survival analysis. PM&R. 2016;  8:580-85.
• Sainani KL. Making sense of intention-to-treat. PM&R. 2010;2:209-13.

Thanks

Thanks to Caitlin Goodrich for the episode topic tip!

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

Are we all secretly ageist when it comes to dating? We put the stereotype that older men prefer younger women under the microscope using data from thousands of blind dates. What we found surprised us: the “age penalty” was real but microscopic, women wanted younger partners too, and hard age cutoffs weren’t so hard after all. Along the way, we unpack statistical significance versus practical importance, play with the infamous “half your age plus seven” rule, and imagine what it would take for love to die out… somewhere around age 628.

Statistical topics

• Discontinuous regression
• Effect sizes
• Extrapolation pitfalls
• Linear regression
• Logistic regression
• Odds ratios
• Open data
• Statistical significance vs. practical significance

Methodological morals

• “Do not be swept off your feet by statistical significance. Tiny effects in bed are still tiny.”
• “Fancy units sound smart, but plain English wins hearts.”

Show Notes Technical Appendix (with step-by-step explanations)

References

• Eastwick PW, Finkel EJ, Meza EM, Ammerman K. No gender differences in attraction to young partners: A study of 4500 blind dates. Proc Natl Acad Sci U S A. 2025 Feb 4;122(5):e2416984122. 
• Matchmaking Dataset and Code on Open Science Framework: https://osf.io/rkm2d/?view_only=a0fe91dae0464077af7772e6890a8151
• Nuzzo RL. Communicating measures of relative risk in plain English. PM&R. 2022 Feb;14(2):283-7.
• O'Rell, Max. Her Royal Highness, Woman: And His Majesty--Cupid. Abbey Press, 1901.
• Sainani KL. Logistic regression. PM&R. 2014 Dec;6(12):1157-62.
• Sainani KL. Understanding odds ratios. PM&R. 2011 Mar;3:263-7. 
• Sainani KL. Clinical versus statistical significance. PM&R. 2012 Jun;4:442-5.

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

ChatGPT is melting our brainpower, killing creativity, and making us soulless — or so the headlines imply. We dig into the study behind the claims, starting with quirky bar charts and mysterious sample sizes, then winding through hairball-like brain diagrams and tens of thousands of statistical tests. Our statistical sleuthing leaves us with questions, not just about the results, but about whether this was science’s version of a first date that looked better on paper.

Statistical topics

• ANOVA
• Bar graphs
• Data visualization 
• False Discovery Rate correction
• Multiple testing
• Preprints
• Statistical Sleuthing

Methodological morals

• "Treat your preprints like your blind dates. Show up showered and with teeth brushed."
• "Always check your N. Then check it again."
• "Never make a bar graph that just shows p-values. Ever."

Link to paper

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

Can correcting misinformation make it worse? The “backfire effect” claims that debunking myths can actually make false beliefs stronger. We dig into the evidence — from ghost studies to headline-making experiments — to see if this psychological plot twist really holds up. Along the way, we unpack interaction effects, randomization red flags, and what happens when bad citations take on a life of their own. Plus: dirty talk analogies, statistical sleuthing, and why “familiarity” might be your brain’s sneakiest trick.

Statistical topics

• Computational replication
• Replication
• Block randomization
• Problems in randomization
• Bad citing
• Interactions in regression

Unpublished "Ghost Paper"

• PDF retrieved from the Wayback Machine

Citations

• Nyhan B, Reifler J. When corrections fail: The persistence of political misperceptions. Political Behavior. 2010;32:303–330.
• Skurnik I, Yoon C, Schwarz N. “Myths & Facts” about the flu: Health education campaigns can reduce vaccination intentions. Unpublished manuscript, PDF posted separately.
• Schwarz N, Sanna LJ, Skurnik I, et al. Metacognitive experiences and the intricacies of setting people straight: Implications for debiasing and public information campaigns. Advances in Experimental Social Psychology. 2007;39:127–61.
• Lewandowsky S, Ecker UKH, Seifert CM, et al. Misinformation and its correction: Continued influence and successful debiasing. Psychological Science in the Public Interest. 2012;13:106–131.
• Pluviano S, Watt C, Della Sala S. Misinformation lingers in memory: Failure of three pro-vaccination strategies. PLOS ONE. 2017;12:e0181640.
• Pluviano S, Watt C, Ragazzini G, et al. Parents’ beliefs in misinformation about vaccines are strengthened by pro‑vaccine campaigns. Cognitive Processing. 2019;20:325–31.
• Wood T, Porter E. The elusive backfire effect: Mass attitudes’ steadfast factual adherence. Political Behavior. 2019;41:135–63.
• Nyhan B, Porter E, Reifler J, Wood TJ. Taking fact-checks literally but not seriously? The effects of journalistic fact-checking on factual beliefs and candidate favorability. Political Behavior. 2020;42:939–60.
• Ecker UKH, Hogan JL, Lewandowsky S. Reminders and repetition of misinformation: Helping or hindering its retraction? Journal of Applied Research in Memory and Cognition. 2017;6:185–92.
• Swire B, Ecker UKH, Lewandowsky S. The role of familiarity in correcting inaccurate information. Journal of Experimental Psychology: Learning, Memory, and Cognition. 2017;43:1948–61.
• Ecker UKH, O’Donnell M, Ang LC, et al. The effectiveness of short- and long-format retractions on misinformation belief and recall. British Journal of Psychology. 2020;111:36–54.
• Ecker UKH, Sharkey CXM, Swire-Thompson B. Correcting vaccine misinformation: A failure to replicate familiarity or fear-driven backfire effects. PLOS ONE. 2023;18:e0281140.
• Cook J, Lewandowsky S. The Debunking Handbook. University of Queensland. 2011.
• Lewandowsky S, Cook J, Ecker UKH, et al. The Debunking Handbook 2020. Available at https://sks.to/db2020. 
• Swire‑Thompson B, DeGutis J, Lazer D. Searching for the backfire effect: Measurement and design considerations. Journal of Applied Research in Memory and Cognition. 2020;9:286–99.

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) -

Loyal, funny, hot — you’ve probably got a wish list for your dream partner. But does checking all your boxes actually lead to happily ever after? In this episode, we dive into a massive global study that put the “ideal partner” hypothesis to the test. Do people really know what they want, and does getting it actually make them happier? We explore surprising statistical insights from over 10,000 romantics in 43 countries, from mean-centering and interaction effects to the good-catch confounder. Along the way, we dig into dessert metaphors, partner boat-count regression models, and the one trait that people say doesn’t matter — but secretly makes them happiest.

Statistical topics

• Regression
• Random Slopes and Intercepts (Random Effects) in Regression
• Standardized Beta Coefficients in Regression
• Interaction Effects in Regression
• Mean Centering
• Exploratory Analyses

Methodological morals

“Good science bares it all.”

“When the world isn't one size fits all, don't fit just one line; use random slopes and intercepts.”

References

• Eastwick PW, Sparks J, Finkel EJ, Meza EM, Adamkovič M, Adu P, Ai T, Akintola AA, Al-Shawaf L, Apriliawati D, Arriaga P, Aubert-Teillaud B, Baník G, Barzykowski K, Batres C, Baucom KJ, Beaulieu EZ, Behnke M, Butcher N, Charles DY, Chen JM, Cheon JE, Chittham P, Chwiłkowska P, Cong CW, Copping LT, Corral-Frias NS, Ćubela Adorić V, Dizon M, Du H, Ehinmowo MI, Escribano DA, Espinosa NM, Expósito F, Feldman G, Freitag R, Frias Armenta M, Gallyamova A, Gillath O, Gjoneska B, Gkinopoulos T, Grafe F, Grigoryev D, Groyecka-Bernard A, Gunaydin G, Ilustrisimo R, Impett E, Kačmár P, Kim YH, Kocur M, Kowal M, Krishna M, Labor PD, Lu JG, Lucas MY, Małecki WP, Malinakova K, Meißner S, Meier Z, Misiak M, Muise A, Novak L, O J, Özdoğru AA, Park HG, Paruzel M, Pavlović Z, Püski M, Ribeiro G, Roberts SC, Röer JP, Ropovik I, Ross RM, Sakman E, Salvador CE, Selcuk E, Skakoon-Sparling S, Sorokowska A, Sorokowski P, Spasovski O, Stanton SCE, Stewart SLK, Swami V, Szaszi B, Takashima K, Tavel P, Tejada J, Tu E, Tuominen J, Vaidis D, Vally Z, Vaughn LA, Villanueva-Moya L, Wisnuwardhani D, Yamada Y, Yonemitsu F, Žídková R, Živná K, Coles NA. A worldwide test of the predictive validity of ideal partner preference matching. J Pers Soc Psychol. 2025 Jan;128(1):123-146. doi: 10.1037/pspp0000524
• Love Factually Podcast: https://www.lovefactuallypod.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) -

It’s our first stats reunion! In this special review episode, we revisit favorite concepts from past episodes—p-values, multiple testing, regression adjustment—and give them fresh personalities as characters. Meet the seductive false positive, the clingy post hoc ex, and Charlotte, the well-meaning but overfitting idealist.

Statistical topics

• Bar charts vs Box plots
• Bonferroni correction
• Confounding
• False positives 
• Multiple testing
• Multivariable regression
• Outcome switching
• Over-adjustment
• Post hoc analysis
• Pre-registration
• Residual confounding
• Statistical adjustment using regression
• Subgroup analysis 
• Unmeasured confounding

References

• Nuzzo RL. The Box Plots Alternative for Visualizing Quantitative Data. PM R. 2016 Mar;8(3):268-72. doi: 10.1016/j.pmrj.2016.02.001. Epub 2016 Feb 15. PMID: 26892802.
• Sainani KL. The problem of multiple testing. PM R. 2009 Dec;1(12):1098-103. doi: 10.1016/j.pmrj.2009.10.004. PMID: 20006317.

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

Could a preteen vaccine wipe out a global cancer? In this episode, we examine the bold claim that cervical cancer could be eradicated in much of the world by the end of the century—thanks to the highly effective HPV vaccine. We unpack statistical modeling, microsimulations, and how Markov chains make good date-night conversation. We also explore why vaccine uptake has been uneven, how a splash of vinegar is helping screen for cancer in low-resource countries, and why HPV isn’t just a women’s issue—it now causes more cancer in men than in women. Plus: dangerously tight corsets, allegedly breast-squeezing nuns, and the Cosmo quote we wish we’d written ourselves.

Statistical topics:

• Cancer surveillance
• Markov models
• Microsimulation models
• Sensitivity analyses
• Passive surveillance
• Background rates
• Case reports and case series

Methodologic morals:

• “When reality is too complex to test, let microsimulations do the rest.”
• “Case reports are medicine's equivalent to see something, say something. They call for hard data, not hysteria.”

Citations:

• No cervical cancer cases detected in vaccinated women following HPV immunisation. University of Strathclyde, January 22, 2024.
• Palmer TJ, Kavanagh K, Cuschieri K, et al. Invasive cervical cancer incidence following bivalent human papillomavirus vaccination: a population-based observational study of age at immunization, dose, and deprivation. J Natl Cancer Inst. 2024;116:857-65.
• Rigoni-Stern. Statistical facts about cancers on which Doctor Rigoni-Stern based his contribution to the Surgeons' Subgroup of the IV Congress of the Italian Scientists on 23 September 1842. (translation). Stat Med. 1987;6:881-4.
• Gordan JA, Lenkei SC. Cleanliness, Continence, Constancy, and Cervical Carcinoma. Can Med Assoc J. 1964;90:1132.
• zur Hausen H. Condylomata acuminata and human genital cancer. Cancer Res. 1976;36:794.
• Walboomers JM, Jacobs MV, Manos MM, et al. Human papillomavirus is a necessary cause of invasive cervical cancer worldwide. J Pathol. 1999;189:12-9.
• Chesson HW, Dunne EF, Hariri S, et al. The estimated lifetime probability of acquiring human papillomavirus in the United States. Sex Transm Dis. 2014;41:660-4.
• Sullivan, Morgan. Let’s Have a Little Chat About the HPV Vaccine. Cosmopolitan. March 19, 2025.
• Burger EA, Kim JJ, Sy S, et al. Age of Acquiring Causal Human Papillomavirus (HPV) Infections: Leveraging Simulation Models to Explore the Natural History of HPV-induced Cervical Cancer. Clin Infect Dis. 2017;65:893-99.
• Canfell K. Towards the global elimination of cervical cancer. Papillomavirus Res. 2019;8:100170.
• World Health Organization. Global strategy to accelerate the elimination of cervical cancer as a public health problem. November 17, 2020.
• Hall MT, Simms KT, Lew JB, et al. The projected timeframe until cervical cancer elimination in Australia: a modelling study. Lancet Public Health. 2019;4:e19-e27.
• Burger EA, Smith MA, Killen J, et al. Projected time to elimination of cervical cancer in the USA: a comparative modelling study. Lancet Public Health. 2020 Apr;5(4):e213-e222.
• Brisson M, Kim JJ, Canfell K, et al. Impact of HPV vaccination and cervical screening on cervical cancer elimination: a comparative modelling analysis in 78 low-income and lower-middle-income countries. Lancet. 2020;395:575-90.
• Escabí-Wojna E, Alvelo-Fernández PM, Suárez E, et al. Sex differences in parental reasons for lack of intent to initiate HPV vaccination among adolescents ages 13-17 years: National Immunization Survey - Teen 2019-2021. Vaccine. 2025;44:126584. (see supplement) 
• Szilagyi PG, Albertin CS, Gurfinkel D, et al. Prevalence and characteristics of HPV vaccine hesitancy among parents of adolescents across the US. Vaccine. 2020;38:6027-6037.
• LaPook, Jonathan. Is the HPV Vaccine Safe? CBS Evening News. August 18, 2009.
• Slade BA, Leidel L, Vellozzi C, et al. Postlicensure safety surveillance for quadrivalent human papillomavirus recombinant vaccine. JAMA. 2009;302:750-7.
• Kharabsheh S, Al-Otoum H, Clements J, et al. Mass psychogenic illness following tetanus-diphtheria toxoid vaccination in Jordan. Bull World Health Organ. 2001;79:764-70.
• Jones TF, Craig AS, Hoy D, et al. Mass psychogenic illness attributed to toxic exposure at a high school. N Engl J Med. 2000;342:96-100.
• Buttery JP, Madin S, Crawford NW, et al. Mass psychogenic response to human papillomavirus vaccination. Med J Aust. 2008;189:261-2.
• Clements CJ. Gardasil and mass psychogenic illness. Aust N Z J Public Health. 2007;31:387.
• Simas C, Munoz N, Arregoces L, et al. HPV vaccine confidence and cases of mass psychogenic illness following immunization in Carmen de Bolivar, Colombia. Hum Vaccin Immunother. 2019;15:163-66.
• Larson HJ. Japan's HPV vaccine crisis: act now to avert cervical cancer cases and deaths. Lancet Public Health. 2020;5:e184-e185.
• Brinth LS, Pors K, Theibel AC, Mehlsen J. Orthostatic intolerance and postural tachycardia syndrome as suspected adverse effects of vaccination against human papilloma virus. Vaccine. 2015;33:2602-5.
• Large well-done studies following up on case reports and passive surveillance:
• Phillips A, Hickie M, Totterdell J, Brotherton J, Dey A, Hill R, Snelling T, Macartney K. Adverse events following HPV vaccination: 11 years of surveillance in Australia. Vaccine. 2020;38:6038-46.
• Arnheim-Dahlström L, Pasternak B, Svanström H, et al.

Today’s deep dive: the surprisingly serious science of penis size. Using self-report surveys, objective measurements, and a healthy dose of old-school statistics, we ask: How do you get clean data on gentlemen’s goods?Along the way, we explore social desirability bias, survey design tricks, and what happens when science meets insecurity. You’ll never look at a Starbucks cup the same way again.

Statistical topics

• Social desirability bias
• Selection bias
• Volunteer Bias
• Descriptive Statistics
• Right-Skewed Distributions
• Strategies to improve accuracy in self-report data

Methodological morals

• “When answers aim to please, truth takes its leave.”
• “Without descriptive statistics, you'll never know if you measure up.”

References

• CROWNE DP, MARLOWE D. A new scale of social desirability independent of psychopathology. J Consult Psychol. 1960;24:349-354. doi:10.1037/h0047358
• Gebhard, P.H. and Johnson, A.B., 1998. The Kinsey data: Marginal tabulations of the 1938-1963 interviews conducted by the Institute for Sex Research. Indiana University Press.
• Herbenick D, Reece M, Schick V, Sanders SA. Erect penile length and circumference dimensions of 1,661 sexually active men in the United States. J Sex Med. 2014;11(1):93-101. doi:10.1111/jsm.12244
• Johnston, L., McLellan, T., & McKinlay, A. (2014). (Perceived) size really does matter: Male dissatisfaction with penis size. Psychology of Men & Masculinity, 15(2), 225–228. https://doi.org/10.1037/a0033264
• King BM. The Influence of Social Desirability on Sexual Behavior Surveys: A Review. Arch Sex Behav. 2022;51(3):1495-1501. doi:10.1007/s10508-021-02197-0
• King BM. Average-Size Erect Penis: Fiction, Fact, and the Need for Counseling. J Sex Marital Ther. 2021;47(1):80-89. doi:10.1080/0092623X.2020.1787279
• King BM, Duncan LM, Clinkenbeard KM, Rutland MB, Ryan KM. Social Desirability and Young Men's Self-Reports of Penis Size. J Sex Marital Ther. 2019;45(5):452-455. doi:10.1080/0092623X.2018.1533905
• Larson, R.B., 2019. Controlling social desirability bias. International Journal of Market Research, 61(5), pp.534-547.
• Stodel, M. (2015). But What Will People Think?: Getting beyond Social Desirability Bias by Increasing Cognitive Load. International Journal of Market Research, 57(2), 313-322. https://doi.org/10.2501/IJMR-2015-024   (Original work published 2015)

Spreadsheet with Penis Length Data

Our online courses and programs: 

• 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 

Find us on social:

Kristin -  LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

• (00:00) - Introduction

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 

• Training and test sets
• Statistical adjustment
• Overfitting 
• Plagiarism
• Proper citing practices
• References to nowhere

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. 

The 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.
• Reference 95 and 37 is a review article: Danby FW. Nutrition and aging skin: Sugar and glycation. Clin. Dermatol. 2010;28: 409–11. Sentence: “...tight glycemic control can drop glycated collagen formation by 25% in 4 months.” No citation given.
• The origi...