decision rule reject if the -value is smaller than some significance level the -value is the probability thatunder the (randomtest statistic takes value as extreme as or more extreme than the one observed in particularif is the observed outcome of the test statistic then -value left one-sided testp :ph [ ]right one-sidedp :ph [ ]two-sidedp :min{ ph [ ] ph [ ]the smaller the -valuethe greater the strength of the evidence against provided by the data as rule of thumbp suggestive evidencep reasonable evidencep strong evidence whether the first or the second decision rule is usedone can make two types of errorsas depicted in table table type and ii errors in hypothesis testing true statement decision is true is true accept correct type ii error reject type error correct the choice of the test statistic and the corresponding critical region involves multiobjective optimization criterionwhereby both the probabilities of type and type ii error shouldideallybe chosen as small as possible unfortunatelythese probabilities compete with each other for exampleif the critical region is made larger (smaller)the probability of type ii error is reduced (increased)but at the same time the probability of type error is increased (reducedsince the type error is considered more seriousneyman and pearson [ suggested the following approachchoose the critical region such that the probability of type ii error is as small as possiblewhile keeping the probability of type error below predetermined small significance level remark (equivalence of decision rulesnote that decision rule and are equivalent in the following sensereject if falls in the critical regionat significance level reject if the -value is significance level significance level