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.

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.