You will be reminded how to spot and avoid statistical biases and fallacies that are common to healthcare actuarial data and problems. We plan to explain various statistical biases and fallacies and for each, show one or two examples of how they might pop up in healthcare data and what strategies can be used to avoid/account for them. Likely topics include Simpson's paradox, regression to the mean, base rate fallacy, sampling bias, survivor bias, Berkson's fallacy, and others.
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