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In 2012, a viral TED Talk promised that standing like a superhero for just two minutes would alter your hormones, boost risk tolerance, and turn you into an executive power player. The catch? The study behind it had a tiny sample of 42 undergraduates, a menu of post-hoc decisions, and an effect size that vanished the second independent researchers tried to replicate it. This week, we dive into the rise and dramatic fall of "Power Posing," unpacking p-hacking, researcher degrees of freedom, and how the Open Science movement brought down one of pop psychology’s biggest fads.
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According to the uncorrected output of an fMRI scanner, a dead, ice-packed Atlantic salmon was actively reading human emotions. This week, we dive into Craig Bennett’s legendary 2009 Ig Nobel-winning study to explore how running 140,000 simultaneous statistical tests on pure thermal background noise can manufacture phantom brain activity out of thin air. We break down the Multiple Comparisons Problem, spatial autocorrelation, and why modern high-dimensional data analytics needs a lot more than a prayer to separate true signals from dead fish.
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Can eating a bar of dark chocolate every day actually accelerate weight loss by 10%? In 2015, global news headlines proclaimed it as a breakthrough scientific fact. But behind the viral media storm was a deliberate honey-pot experiment designed by a scientist-turned-journalist to expose how easily health media and pay-to-play journals can be fooled by textbook statistical tricks.
In this inaugural episode, we perform a complete autopsy on the "Chocolate Weight Loss Study," dissecting the subtle mathematical trap that guaranteed a "statistically significant" false positive before a single participant was even recruited.
From the publisher's feed
A Biostatistician’s Guide to Scientific Bullshit
The world is drowning in bad science, viral health claims, and headline-grabbing studies that crumble under the slightest…
Hosted by a trained biostatistician and practicing data leader, this show strips away the media spin, pulls back the curtain on academic publishing, and exposes the methodological flaws behind headline-grabbing research. Through sharp single-paper autopsies and friendly expert debates, each episode dissects the statistical crimes—from tiny sample sizes and $p$-hacking to flawed spatial models—that turn random noise into global news.
Whether you’re a researcher, a data practitioner, or just a professional skeptic tired of bad science reporting, this podcast gives you the practical mental toolkit to spot scientific bullshit in the wild.