The B FIT Podcast w/ Connor Murphy
On February 15th, Co-founder and CEO of The Broken Science Initiative, Emily Kaplan, went onto the B FIT Podcast with […]
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.
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.
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.
Understanding All-Cause Mortality
In this video Emily explains all-cause mortality, and how it impacts decision making for medical intervention.
Understanding Medical Tests
In this video, Emily explains how to understand medical tests. She dives into both sensitivity and specificity of testing.
Induction Vs. Deduction
In this next part, Emily breaks down the difference between induction and deduction.
Section 230: The Hidden Battleground Where Tech Giants, Government and Free Speech Collide
Delving into the murky waters of online censorship, this piece uncovers the genesis of Section 230, the law that inadvertently became the root of a heated battle between free expression and digital regulation. Unraveling the complex interplay between government oversight and technological innovation, this piece explores the implications of a seemingly innocuous legislation that now governs the very nature of online discourse.
