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.
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