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Normal Curves: Sexy Science, Serious Statistics

Normal Curves: Sexy Science, Serious Statistics

By: Regina Nuzzo and Kristin Sainani
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Normal Curves is a podcast about sexy science & serious statistics. Ever try to make sense of a scientific study and the numbers behind it? Listen in to a lively conversation between two stats-savvy friends who break it all down with humor and clarity. Professors Regina Nuzzo of Gallaudet University and Kristin Sainani of Stanford University discuss academic papers journal club-style — except with more fun, less jargon, and some irreverent, PG-13 content sprinkled in. Join Kristin and Regina as they dissect the data, challenge the claims, and arm you with tools to assess scientific studies on your own.©2026 Regina Nuzzo & Kristin Sainani Science Social Sciences
Episodes
  • Cancer Blood Tests: Are they ready for primetime? Part 1

    Cancer Blood Tests: Are they ready for primetime? Part 1
    Jun 15 2026
    Can a single tube of blood really detect dozens of cancers before symptoms appear? We dive into the science behind Galleri, a blood test that claims to detect more than 50 types of cancer from a simple blood draw. Recent headlines about the test ranged from “breakthrough” to “bust” after the release of results from a massive randomized clinical trial. In this Part 1 episode, we explore cell-free DNA, DNA methylation, machine learning, sensitivity, specificity, and positive predictive value. Along the way, we revisit the prenatal screening revolution, ask why detecting cancer earlier doesn’t always help patients, and learn how escaped DNA convicts end up swimming in a giant molecular pool party. And for the first time ever, Normal Curves ends on a cliffhanger: we’ll save the controversial results of that landmark trial for Part 2.Statistical topicscancer screeningcase-control studiescounterfactualsmachine learningnegative predictive valueoverdiagnosispositive predictive valuerandomized clinical trialsscreening testssensitivity and specificityvalidationReferencesBianchi DW, Chudova D, Sehnert AJ, et al. Noninvasive prenatal testing and incidental detection of occult maternal malignancies. JAMA. 2015; 314:162-9. Liu MC, Oxnard GR, Klein EA, et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann Oncol. 2020. 31:745-59. Schrag D, Beer T, McDonnell C et al. Blood-based tests for multicancer early detection (PATHFINDER): a prospective cohort study. The Lancet. 402: 1251-60.Giridhar KV, et al. Safety and performance results from PATHFINDER 2, a registrational study of a multi-cancer early detection test in an intended-use population. Presented at the 2026 American Society of Clinical Oncology (ASCO) Annual Meeting. May 2026.Statistic discussed in the episodePATHFINDER 2 investigators reported that adding Galleri to routine screening increased the number of screen-detected cancers by 6.5-fold. This figure compares 31 cancers detected through USPSTF-recommended screening (for breast, cervical, lung, and colon) with 204 cancers detected when Galleri was added, counting the same 31 conventional-screening cancers in both totals. Thus, describing the increase as 6.5-fold is misleading, since the combination of Galleri plus conventional screening is, by definition, guaranteed to detect at least as many cancers as conventional screening alone. Moreover, everyone in the study received Galleri, whereas conventional screening depended on which tests participants happened to be due for and completed during the study period. The comparison therefore does not involve two equally applied screening strategies.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/XRegina - LinkedIn & ReginaNuzzo.com(00:00) - - Introduction (00:44) - - The Holy Grail of Cancer Testing (04:31) - - Headlines: Same Data, Opposite Stories (07:38) - - How Cell-Free DNA Works (13:54) - - DNA Methylation: GRAIL's Fingerprint (15:19) - - The Origin Story (22:18) - - The Pathfinder Studies (35:01) - - The Paradox: Why Earlier Detection Doesn't Always Help (40:32) - - The Cliffhanger
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    43 mins
  • Odds Ratios: Do most people get them wrong?

    Odds Ratios: Do most people get them wrong?
    Jun 1 2026
    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 topicsbinary outcomescase-control studieslogistic regressionodds ratiosrisk ratiosodds 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.”ReferencesBleich 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 ratiosReporting risk ratios directly when appropriateConverting 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.aspxRR = 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= 0.94Thus, 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%=0.92Correction: 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/XRegina - 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 (22:08) - How Logistic Regression Works (31:53) - The Video: Researchers Made the Mistake Themselves (36:30) - The Cardiac Catheterization Study (39:24) - The New York Times Printed a Correction (46:10) - Using OR and RR Interchangeably for Case Control (47:00) - Reye Syndrome and Aspirin (49:37) - Rating the Claim and Methodological Morals
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    54 mins
  • Coffee and the Heart: Is caffeine a trigger for AFib?

    Coffee and the Heart: Is caffeine a trigger for AFib?
    May 18 2026

    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
    • (23:24) - - CRAVE Results
    • (30:07) - - Catching the Coffee Cheaters
    • (37:01) - - The DECAF Trial
    • (41:30) - - Time-to-Event Outcomes
    • (43:21) - - Hazard Ratios: Balance Beams Over Shark Tanks
    • (47:06) - - DECAF Results: Team Coffee Wins
    • (50:38) - - Why Would Coffee Be Protective?
    • (53:57) - - Rating the Claim
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    57 mins
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