Simply put, a p-value is a measure of the likelihood that the results of a study are due to the hypothesis, and not simply a result of chance. It compares the “null hypothesis,” the idea that the thing being studied has no effect, vs the “alternative hypothesis,” the thing being tested. So if the p-value is low, the data is thought to be significant. However, the p-value does not validate the effectiveness of the thing being studied, it simply claims to shows that the results were not due to chance.

Frighteningly, scientists, researchers, and medical professionals misinterpret the meaning of p-values but place extreme faith in them.

In this edition Malcolm explains how study results can be carefully worded, to imply results that are actually contrary to data.

On his podcast, Emily and Dr. Drew talked at length about the overuse of statins to treat coronary heart disease.

Faults with a Stanford study's claims that a vegan diet is better for cardio metabolic health.

Using surrogate end-points for clinical trials represents a massive boost for pharmaceutical company profits. So, they work hard, behind the scenes, to ensure that this happens

Much planning and setup is needed to convince the world medical treatments are effective, or even necessary in the first place.

In this presentation from 2019, Greg Glassman tells the story of how bad science, primarily in the form of corruption, led him to make business decisions to fight to protect his affiliate gyms.

Emily explains the strengths, weaknesses, and ways to interpret observational studies. These types of studies can be useful for identifying links between things, and then generating hypotheses. However, the results of any observational study are strictly corollary, and do not prove cause.