# Holiday Survival Guide Part 2: The survey study edition

Page: https://stenobird.com/podcast/normal-curves-sexy-science-serious-statistics-7212156/holiday-survival-guide-part-2-the-survey-study-edition
Text version: https://stenobird.com/podcast/normal-curves-sexy-science-serious-statistics-7212156/holiday-survival-guide-part-2-the-survey-study-edition.md
Podcast: [Normal Curves: Sexy Science, Serious Statistics](https://stenobird.com/podcast/normal-curves-sexy-science-serious-statistics-7212156)
Published: 2025-12-01T12:00:00+00:00
Episode link: https://www.normalcurves.com/holiday-survival-guide-part-2-the-survey-study-edition/
Audio file: https://op3.dev/e/media.transistor.fm/e9563653/9ff83590.mp3
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JSON: https://stenobird.com/v1/public/podcasts/normal-curves-sexy-science-serious-statistics-7212156/episodes/holiday-survival-guide-part-2-the-survey-study-edition
Duration seconds: 3762

## Resource

Does the temperature of your coffee six months ago really predict whether you feel gassy today? This week we dissect a new nutrition survey study on hot and cold beverage habits that claims to connect drink temperature with gut symptoms, anxiety, and more—despite relying on year-old memories and a blizzard of statistical tests. It’s the perfect case study for our Holiday Survival Guide Part 2, where we teach you how to talk with Uncle Joe at the dinner table about one of the most common—and most fraught—study designs in science: cross-sectional surveys. We walk through our easy checklist for making sense of results, show how recall bias and measurement error can skew the story, and reacquaint you with nonmonogamous Multiple-Testing Dude, who’s been very busy in this dataset. A friendly, practical guide to spotting when researchers are just torturing the data until it confesses. Statistical topics Confounding Cross-sectional studies False positives Measurement error Multiple testing PICOT / PIVOT framework Recall bias Research hypotheses Sample size and power Signal vs. noise SMART framework Statistical significance Subgroup analyses Survey design Transparency and trustworthiness Methodological morals “When your measurement starts with ‘think back to last winter’ you might as well use a random number generator.” “If the effect is only significant in certain subgroups in certain seasons for certain outcomes, it might just be a bad case of gas.” References Wu T, Doyle C, Ito J, et al. Cold Exposures in Relation to Dysmenorrhea among Asian and White Women . Int J Environ Res Public Health . 2023;21(1):56. Published 2023 Dec 30. doi:10.3390/ijerph21010056 Wu T, Ramesh N, Doyle C, Hsu FC. Cold and hot consumption and health outcomes among US Asian and White populations . Br…

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