Insights into the concepts of hypothesis testing (Part 4)

Type II error – Consumer’s risk

In continuation to yesterday’s discussion, we don’t have the luxury of knowing the whole population. Unknown population means unknown values of population parameters. But through hypothesis testing, confirmatory inferences about the unknown population parameters can be made. But when we accept a null hypothesis either we make a correct decision of accepting a true null hypothesis or an error of accepting a false null hypothesis. The error of accepting a false null hypothesis is called Type II error. This error is not considered serious as Type I error, as the status quo is not disturbed as a consequence of this error. It can be compared to accepting a bad lot due to variations in sampling also called as Consumer’s Risk. A bad lot is wrongfully accepted as it confirms to the decision rule developed for the acceptance of a lot on the basis of sample drawn from the lot. The probability of this error is called beta.