## P-Values Are Tough And S-Values Can Help

A discussion about what P-values are including common misinterpretations and how something called an S-value can better help us interpret P-values.

A discussion about what P-values are including common misinterpretations and how something called an S-value can better help us interpret P-values.

Discussions praising the efficiency of randomized trials are widespread, however, few of these discussions take a close look at some of the common assumptions that individuals hold regarding randomized trials. And unfortunately, these common assumptions may be based on outdated evidence and simplistic ideas.

Another misinterpretation of what statistical power is and how trial results should be reported in a popular journal.

The Bradford Hill Criteria are commonly used as a checklist to argue for causality when randomized trials aren’t possible. However, the originator of these viewpoints never intended for them to be used this way. In this post, I examine the shortcomings of using these viewpoints as a checklist in the real world.

Analytic statistics are commonly used to make inferences from the data. However, they are often misused because of misconceptions about what they do. In this post, I discuss how standard error is commonly misused in clinical trials.

Most studies aim to achieve high statistical power and precision by increasing sample sizes. Many researchers will conclude that there is no effect if they get a nonsignificant result in a high-powered study. In this post, I discuss why this is incorrect.

A look at a time when Bayesian data analysis went off the rails.

The number needed to treat is a popular statistic used in medicine, but it has several problems associated with the way it is used and in this blog post, I discuss some of those problems and possible solutions.

False positives are something we must think about when making inferences, but not everyone is aware that the probability of getting a false positive skyrockets as more comparisons are made. In this blog post, I discuss the problem of false positives and controlling error rates.

Meta-analyses are widely used to summarize and examine treatment effects. However, few researchers examine the relationship between power and meta-analyses. In this post, I discuss power conceptually and mathematically. Then I explain how different models impact power and the problems with under-powered meta-analyses.