On the influence of misclassified data on results of goodness of fit testing

In this paper we focus on the chi-square test of goodness of fit, which compares an observed
discrete distribution to an expected known one. We show that the results of this test, using the
common Pearson statistic, are very sensitive to misclassified observations between two or more
categories. We also propose a general rule of thumb for analysing data set stability with respect
to such classification errors. Practical analysis of a real example illustrates our purpose.