Mindless Statistics
Gerd dives into the history of statistical analysis in his field of psychology, which has led to a paradigm of rituals, in place of meaningful thought.
Science Is Successful Prediction
Greg Glassman kicked off the 2024 BSI Epistemology Camp with this presentation. Greg’s talk centers around the ‘breaking point’ from modern science to post modern science.
Bayesian Vs. Frequentist Statistics
In this video Emily explains the difference between a Bayesian approach and a frequentist approach to analyzing statistics. A Bayesian analysis looks at prior probabilities combined with data to determine the probability that the hypothesis is true. A frequentist analysis compares the hypothesis to the null-hypothesis, a yes/no approach, to determine if the data could support the null-hypothesis. It then ranks the data with a P-value, but it actually says nothing about the hypothesis being true.
Losing Faith In Medical Research
In February of 2023 Dr. Kendrick spoke at BSI’s event in Phoenix, AZ. Malcom’s presentation recaps the history of the diet-heart hypothesis, the supposed link between cholesterol and cardiovascular death, and how drug companies have fooled doctors into prescribing medications that are harming patients rather than helping them.
P-Values Explained
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.
Ask Dr. Drew: Is Long-Term Statin Use Accelerating Heart Failure?
On his podcast, Emily and Dr. Drew talked at length about the overuse of statins to treat coronary heart disease.
Bad Science’s Threat To Fitness – Full Video
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.
The Purpose and Limitations of Observational Studies
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.
Understanding All-Cause Mortality
In this next video, BSI’s Emily Kaplan explains all-cause mortality, and how it should be included when considering any medical treatment.
Correlation Vs. Causation
In this second installment, BSI’s Emily Kaplan breaks down the difference between corollary studies and causal studies. Corollary studies can show relationships between data sets, they do not establish cause.
