Uncommon Hypothesis Tests to Debunk Common Misconceptions

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I gave a talk about p-values and hypothesis testing at BIDS. Please check out my slides!

P-values get a large share of the blame for the replication crisis in science. People take for granted that the tests they use work without justifying the leap from data to model. Often, reported p-values are erroneous because the underlying model doesn’t accurately describe the way the data arose. I gave three examples of hypothesis tests I’ve developed where standard methods of analysis have failed: testing the adequacy of pseudo-random number generators for statistical simulations, gender bias in student evaluations of teaching, and risk-limiting election auditing.