![]() The null hypothesis is therefore that dutasteride does not decrease the risk of prostate cancer. In our study example, the hypothesis being tested is whether dutasteride decreases the risk of prostate cancer as detected by biopsy among men at increased risk for prostate cancer. This means that the null hypothesis is appropriately rejected if the probability of a Type I error is <5%. Although a P-value is appropriately considered a statistic interpretable across a range of values, in contemporary experimental studies, “statistical significance” is now conventionally set at a P-value of <0.05. It is not, however, the probability that the null hypothesis is true. Fisher suggested, a P-value is an index for the strength of the evidence for the tested hypothesis against the null hypothesis. ![]() Statisticians Jerzy Neyman and Egon Pearson later described this as a Type I error in contrast to a Type II error where the null hypothesis is accepted when, in actuality, the null hypothesis is false. Stated another way, a P-value is the probability that an observed difference is due to random chance when the null hypothesis is true. P -values were introduced in the first-half of the 20 th century as the probability of rejecting the null hypothesis that a treatment has no effect when, in actuality, the null hypothesis is true.
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