Normal Curves: Sexy Science, Serious Statistics

Can weed protect your brain as you get older? We unpack a study linking lifetime cannabis use to larger brain volumes and better cognitive test scores in older adults—and investigate how those findings became headlines urging readers to “pass the pot to grandma.” Along the way, we explore collapsing categorical variables, confounding, clinically versus statistically significant results, multiple testing, and how a press release subtly changed a story. We also meet permanently stoned mice, learn about Carl Sagan’s surprising connection to cannabis research, and discover how a single missing word can change the entire meaning of a study.
Statistical topics
Categorical variables
Confounding
Effect size
False discovery rate
Healthy volunteer bias
Measurement error
Multiple testing
Observational studies
Recoding variables
Science communication
Statistical vs practical significance

Methodologic Morals
“Always follow the chain from data to result to conclusions and pay attention to the words along the way.”
“Huge samples can give tiny differences delusions of grandeur.”

References
Guha A, Fu Z, Calhoun V, Hutchison KE. Lifetime Cannabis Use Is Associated With Brain Volume and Cognitive Function in Middle-Aged and Older Adults. J Stud Alcohol Drugs. 2026;87(4):739-751. doi:10.15288/jsad.25-00346
Press Release: https://news.cuanschutz.edu/news-stories/study-finds-cannabis-usage-in-middle-aged-and-older-adults-associated-with-larger-brain-volume-better-cognitive-function
Bilkei-Gorzo A, Albayram O, Draffehn A, et al. A chronic low dose of Δ9-tetrahydrocannabinol (THC) restores cognitive function in old mice. Nat Med. 2017;23(6):782-787. doi:10.1038/nm.4311
NY Post: https://nypost.com/2026/02/07/health/cannabis-may-benefit-aging-brains-study-finds/
David Grinspoon: https://www.nytimes.com/2020/07/02/science/lester-grinspoon-dead.html?unlocked_article_code=1.7lA.CyS1.neeqGGNgx8g1&smid=url-share

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) - Opening: The Brain on Drugs

(00:37) - The Cannabis Headline

(03:38) - The Claim: Brain Benefits for Older Adults

(06:53) - The Mouse Study: Pothead Mice vs. Control Mice

(11:25) - From Mice to Humans: UK Biobank

(15:01) - Measurement Problems and Category Collapse

(21:39) - Results: Statistically Significant, Clinically Meaningless

(27:13) - Individual Brain Regions and Cherry-Picking

(32:32) - Cognitive Tests: What Actually Matters

(40:09) - How It Gets Lost in Translation

(45:32) - Rating the Claim

Can the meningitis vaccine do double duty against gonorrhea? We examine a fascinating randomized trial inspired by years of encouraging observational studies—and what happened when those earlier results were finally put to the test. Along the way, we discuss ecological studies, case-control studies, why statisticians distrust phrases like “trend toward significance,” how intention-to-treat differs from per-protocol analysis, and why no amount of statistical adjustment can fully substitute for randomization. Along the way we celebrate outrageously named clinical trials, ask whether “flirting with statistical significance” belongs in a romance novel or a medical journal, and discover that “practice safe statistics” may be the best advice of the episode.

Statistical topics
Case-control studies
Confounding
Ecological studies
Generalizability
Intention-to-treat
Meta-analysis
Null results
Observational studies
P-values
Per-protocol analysis
Randomized clinical trials
Survival analysis

Methodologic Morals
“Don't mistake a p-value of 0.06 for a p-value of 0.04 with bad luck.”
 “Practice safe statistics; randomize whenever possible.”
References
Seib KL, Donovan B, Jin F, et al. Meningococcal B Vaccine to Prevent Neisseria gonorrhoeae Infection. N Engl J Med. 2026; 395: 349-61. 
Petousis-Harris H, Paynter J, Morgan J, et al. Effectiveness of a group B outer membrane vesicle meningococcal vaccine against gonorrhoea in New Zealand: a retrospective case-control study. The Lancet. 2017; 390: 1603–10.
Wang, B., Mohammed, H., Andraweera, P., McMillan, M., Marshall, H., 2024. Vaccine effectiveness and impact of meningococcal vaccines against gonococcal infections: A systematic review and meta-analysis. Journal of Infection. 2024; 89: 106225. 
Molina JM, Bercot B, Assoumou, L, et al. Doxycycline prophylaxis and meningococcal group B vaccine to prevent bacterial sexually transmitted infections in France (ANRS 174 DOXYVAC): a multicentre, open-label, randomised trial with a 2 × 2 factorial design. The Lancet Infectious Diseases. 2024; 24: 1093–1104.
Thng C, Eskandari S, Jin F, et al. Efficacy of the meningococcal vaccine against Neisseria gonorrhoeae: a randomised clinical trial (MenGO). npj Vaccines. 2026.

Still Not Significant | Probable Error: List of funny disguises scientists use to describe p>.05

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

(00:15) - - Introduction

(00:51) - - The Claim: Can a Meningitis Vaccine Prevent Gonorrhea?

(04:10) - - The Biology Behind the Idea

(10:37) - - How Observational Studies Made the Link

(14:44) - - Earlier Clinical Trials: MENGO and DOXYVAC

(17:30) - - Flirting with Significance: A Statistical Rant

(22:40) - - GoGoVax: Study Design and Analysis

(33:31) - - The Shocking Null Result

(35:38) - - Why These Results Don't Generalize

(37:42) - - Rating the Claim

Can you improve your endurance by sitting in a hot tub instead of doing another hard workout? We dig into a study that put elite runners through five weeks of what we affectionately call “endurance hot tubbing” to see whether heat alone could trigger the same cardiovascular adaptations as altitude training. Along the way, we explore crossover study designs, multiple testing, why significant physiological changes don’t necessarily translate into better athletic performance, and how an exploratory regression analysis ended up answering the wrong scientific question. We also discover that serious hot tub research involves surprisingly little relaxation, debate whether bubbles should count as an experimental condition, and learn why distance runners pay good money to sleep in tents with some of the oxygen sucked out.

Statistical topics
best subsets regression
causal inference
crossover studies
exploratory analyses
mediation / mechanisms
model specification
multiple testing
peer review
sample size

(00:15) - Introduction

(00:44) - Can Hot Tubs Replace Exercise?

(01:27) - Crazy Runners and Crazy Training

(05:44) - The Amazon Delivery System Metaphor

(12:12) - From Heat Training to Hot Tubs

(13:33) - The Study: 10 Runners in Hot Water

(22:31) - Results and the VO2 Max Question

(29:26) - Statistical Sleuthing: Best Subsets Regression

(36:50) - The Peer Review Problem

(41:39) - Rating the Claim

Methodologic Morals
“A model can be very good at answering the wrong question.”
 “The label ‘peer reviewed’ does not guarantee ‘carefully reviewed’.”
References
Jenkins EJ, Killick JA, Zerilli O, et al. Long-term passive heat acclimation enhances maximal oxygen consumption via haematological and cardiac adaptation in endurance runners. J Physiol. Published online November 20, 2025. doi:10.1113/JP289874
Stembridge M, Jenkins E. Marathon training: Why hot baths might help you run faster. The Conversation. Published March 16, 2026. doi:10.64628/AB.6vknm9esh

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

(00:15) - Introduction

(00:44) - Can Hot Tubs Replace Exercise?

(01:27) - Crazy Runners and Crazy Training

(05:44) - The Amazon Delivery System Metaphor

(12:12) - From Heat Training to Hot Tubs

(13:33) - The Study: 10 Runners in Hot Water

(22:31) - Results and the VO2 Max Question

(29:26) - Statistical Sleuthing: Best Subsets Regression

(36:50) - The Peer Review Problem

(41:39) - Rating the Claim

Could four one-minute bursts of exercise really improve blood sugar? We try “exercise snacks” ourselves before taking a close look at the clinical trial that inspired headlines. We explain why the study’s main result wasn’t statistically significant, how 34 secondary outcomes complicated the story, and what pre-registration can reveal about a study after it’s published. Along the way, we compare notes on our own exercise-snacking adventures, debate continuous glucose monitors, and ask how much evidence a single study should generate before it becomes health news.

Statistical topics
Crossover design
Multiple testing
Pre-registration
Primary vs secondary outcomes
Randomized controlled trial
Research transparency
Methodologic Morals
“It's good relationship advice to be transparent. It's also good research advice.”
“If the primary outcome is not significant, say it up top.”
References
Babir FJ, Marcotte-Chénard A, Sandilands RE, et al. Exercise snacks performed in real-world settings reduce postprandial hyperglycaemia and glycaemic variability in individuals living with type 2 diabetes: a randomised crossover study. Diabetologia. 2026;69(8):2200-2211. doi:10.1007/s00125-026-06741-2
https://www.washingtonpost.com/wellness/2026/05/28/4-minutes-exercise-day-could-help-control-blood-sugar/
clinicaltrials.gov pre-registration with changes: https://clinicaltrials.gov/study/NCT06382246?term=NCT06382246&rank=1&tab=history&a=1&b=2#version-content-panel

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

(04:06) - The claim: four minutes a day

(07:05) - Our own N of 1 experiments

(13:24) - The study

(21:54) - Primary outcome: complete miss

(25:16) - Secondary outcomes to the rescue?

(35:02) - Statistical sleuthing and transparency

(44:23) - Rating the claim

How do you decide whether a clinical trial “worked”? In Part 2 of our Galleri series, we examine the landmark randomized trial of a blood test designed to detect more than 50 cancers. We explore why different outcome measures led to dramatically different headlines, discuss primary versus secondary outcomes, pre-registration, hierarchical testing, and post hoc analyses, and explain why mortality remains the outcome everyone is waiting for. Along the way, we uncover a statistical mystery involving dozens of missing cancers and discover how a little arithmetic can sometimes reveal more than a press release.
Statistical topics
cancer screening
exploratory analyses
hierarchical testing
missing data
multiple testing
outcome measures
post hoc analyses
pre-registration
primary and secondary outcomes
randomized clinical trials
screening tests

Methodologic Morals
“When the simple numbers don't add up, pay attention. The arithmetic may be trying to tell you something.”
“The first question should not be, did it work? It should be, what counts as success?”

References
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.
Hubbell E, Clarke CA, Aravanis AM, Berg CD. Modeled Reductions in Late-stage Cancer with a Multi-Cancer Early Detection Test. Cancer Epidemiol Biomarkers Prev. 2021;30(3):460-468. doi:10.1158/1055-9965.EPI-20-1134
Neal RD, Johnson P, Clarke CA, et al. Cell-Free DNA-Based Multi-Cancer Early Detection Test in an Asymptomatic Screening Population (NHS-Galleri): Design of a Pragmatic, Prospective Randomised Controlled Trial. Cancers (Basel). 2022;14(19):4818. Published 2022 Oct 1. doi:10.3390/cancers14194818
ASCO slides: https://grail.com/wp-content/uploads/2026/05/Swanton_ASCO-2026_NHS-Galleri_FINAL-Slides-05.26.2026.pdf
UK registry protocol:  https://www.isrctn.com/ISRCTN91431511 
Clinicaltrials.gov protocol: https://clinicaltrials.gov/study/NCT05611632 

Common biases in cancer screening studies
Cancer screening studies are subject to several well-known biases that can make a screening test appear more effective than it actually is. Three of the most important are:
Lead-time bias: Screening advances the time of diagnosis, making survival from diagnosis appear longer even if the patient's lifespan is unchanged. For example, if a screening test detects a Stage II cancer at age 60 that otherwise would have been diagnosed because of symptoms at age 62, but the patient dies at age 68 regardless, survival from diagnosis appears to increase from 6 years to 8 years even though the patient did not live any longer. 
Length bias: Screening preferentially detects slower-growing, less aggressive cancers because they remain detectable for longer than fast-growing cancers. For example, a slow-growing cancer that remains in Stage I for 5 years is much more likely to be found by screening than an aggressive cancer that progresses to symptoms within months. This can make screened patients appear to have better survival simply because screening preferentially found the less aggressive cancers. 
Overdiagnosis: Screening detects cancers that would never have caused symptoms or death during a person's lifetime, leading to unnecessary diagnosis and treatment. For example, a screening test may detect a very slow-growing prostate or thyroid cancer in an older adult that would never have become clinically important if it had remained undiscovered. 

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

(03:39) - The Claim: Not Ready for Primetime

(03:58) - Trial Design: 142,000 Participants

(07:50) - The Primary Outcome Problem

(20:29) - The Primary Endpoint: Complete Miss

(22:14) - Three Arguments for the Defense

(28:29) - - Statistical Sleuthing: Missing Cancers

(41:14) - - The Stage Shift Argument

(50:30) - - Rating the Claim