Statistical Pitfalls and Misinterpretation of Data

Undergraduate / Graduate

This course will provide tools for thinking critically about data and models that constitute evidence, e.g., for the purpose of making predictions, reaching decisions, judging the quality of information, or in the context of scientific research. The course will examine examples of misleading language and graphics and discuss good practices in representing and communicating data. Examples will include such pitfalls as data size effects, false linearity, biased or correlated samples, mistaking correlation for causation, regression to the mean, and Simpson's paradox.