Critical analysis of the popular anti-aging supplement fatty15, dissecting its "science-backed" claims and deceptive marketing tactics.Is fatty15 a “new essential fatty acid” that’s “3× better than omega-3”?Is fatty15 GRAS "certified'?Does fatty15 have any health benefits in humans?We review the evidence, including all the human randomized controlled trials on fatty15.Slides: https://www.jsdatascience.com/fatty15/

In this episode, we revisit the controversial Keto-CTA study about cholesterol, and arterial plaque progression. After the authors released some of their raw data, our analysis reveals the study's flaws are far worse than first thought. John breaks down the egregious errors, from published charts that incorrectly cut plaque progression in half, to statistical models that violate their own assumptions, directly contradicting the authors' claims and invalidating their conclusions.

In this episode, we dive into five statistical concepts everyone should know. We cover regression to the mean, how to evaluate model performance, what medical diagnostic tests really tell you, the Hawthorne effect, and what causation actually means in research.

In part 2 of How to Read Scientific Studies Series, we focus on interpreting the data and conclusions.We break down how results are reported, what statistical significance actually means, and how to spot confounding factors. We also highlight the common tricks and manipulations that influencers use to misrepresent research findings and media headlines.

Separate science from the hype with our guide for vetting scientific studies. Master the critical thinking tools to make true evidence‑based decisions.

Is kombucha truly good for you? We examine the evidence from human clinical trials to understand its effects on the body.

Presentation Available: https://www.jsdatascience.com/podcast_kombucha/

We break down the recently controversial paper “Plaque Begets Plaque, ApoB Does Not” with a critical eye on its statistical flaws.

The most important statistical concepts that everyone should understand. Whether you're into data or just curious about how the world works. From mean vs. median to correlation vs. causation, we discuss it all in simple and applicable terms.