An update on some influential readings from the last few years.
Physics intuition for regression and other methods that minimize squared error. We can imagine springs pulling the model toward the data.
Some retrospective on an eventful 2020, announcing a move from New York to London, a couple book recommendations, and a new design for this website.
Recent arguments against the use of p-values and significance testing are mostly weak. The weak ones are actually arguments against making decisions or mistakes in general, which is impossible.
Model selection can invalidate inference, such as significance tests, but statisticians have recently made progress developing methods to adjust for this bias. This post motivates a conditional approach with a simple screening rule example and introduces an R package that can compute adjusted significance tests.
People often do regression model selection, either by hand or using algorithms like forward stepwise or the lasso. Sometimes they also report significance tests for the variables in the chosen model. But there’s a problem: the reason for p-value significance may just be something called model selection bias.
