The common theory of the new COVID variant is that intrinsic biological factors make it more transmissible. An alternate theory attributes the same dynamics to social explanations like community spread among school-aged children.
A variant of COVID recently grew rapidly in London. Experts have warned this strain may be more transmissible, and governments have enacted more restrictions as a response. But the new variant has not spread rapidly in other locations.
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.
Physics intuition for regression and other methods that minimize squared error. We can imagine springs pulling the model toward the data.
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.
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