<P> In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis (also known as a "false positive" finding), while a type II error is failing to reject a false null hypothesis (also known as a "false negative" finding). More simply stated, a type I error is to falsely infer the existence of something that is not there, while a type II error is to falsely infer the absence of something that is . </P> <P> In statistics, a null hypothesis is a statement that one seeks to nullify with evidence to the contrary . Most commonly it is a statement that the phenomenon being studied produces no effect or makes no difference . An example of a null hypothesis is the statement "This diet has no effect on people's weight ." Usually, an experimenter frames a null hypothesis with the intent of rejecting it: that is, intending to run an experiment which produces data that shows that the phenomenon under study does make a difference . In some cases there is a specific alternative hypothesis that is opposed to the null hypothesis, in other cases the alternative hypothesis is not explicitly stated, or is simply "the null hypothesis is false"--in either event, this is a binary judgment, but the interpretation differs and is a matter of significant dispute in statistics . </P>

Difference between type 1 and type 2 error in hypothesis testing
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