Fisher's Exact Test for Misclassified Data

Fisher's exact test is adapted to handle the misclassified data arising from comparing two binomial populations. The bias-adjusted odds ratio is proposed to account for misclassification errors. Its expected power depends in a nonlinear way on the true sensitivity and specificity of the classification method. The data taken from the no conviction rate of criminality for two types of twin populations was used to illustrate how to calculate true sensitivity and specificity and the expected power of the adjusted odds ratio.