A Humorous Way To Tell The Difference Between False Positive and False Negative Errors

For the people who are statistically challenged this is humorous way to describe the differences between Type I(false positive) and Type II(false negative) errors. I saw this infographic originally on the Marginal Revolution blog. They think the original post was probably over at FlowingData website who gives credit to Jim Thornton’s twitter account. As a person who is seriously considering going without health insurance if the insurance rates go up too much, the question you have to ask yourself is whether you can do a better job minimizing the financial impact of false positive and false negative diagnosis errors than your insurance company. As an example there are a lot of false positives associated with prostate and breast cancer.


"Type I" and "Type II" errors, names first given by Jerzy Neyman and Egon Pearson to describe rejecting a null hypothesis when it’s true and accepting one when it’s not, are too vague for stat newcomers (and in general). This is better. [via]