![]() In this post, I introduce a range of simple, but more sensible, alternatives to the p-value criterion which can overcome the above-mentioned deficiencies. On this point, Wasserstein and Lazar (2016) strongly recommend that the p-value be supplemented or even replaced with other alternatives. ![]() This situation occurs because, while the p-value is a decreasing function of sample size, its threshold ( α) is fixed and does not decrease with sample size. This is especially so when the sample size is large or massive. One of the consequences is that the p-value criterion frequently rejects H0 when it is violated by a practically negligible margin. the conventional values of α (such as 0.05) are arbitrary with little scientific justification.the criterion completely ignores P(D|H1), the compatibility of data with H1 and.the p-value is a decreasing function of sample size.However, as made clear from the statements of the American Statistical Association ( Wasserstein and Lazar, 2016), the p-value criterion as a decision rule has a number of serious deficiencies. The conventional values for this decision threshold include 0.05, 0.10, and 0.01.īy definition, the p-value measures how compatible the sample information is with H0: i.e., P(D|H0), the probability or likelihood of data (D) under H0. The criterion is to reject the null hypothesis (H0) in favour of the alternative (H1), when the p-value is less than the level of significance ( α). In establishing statistical significance, the p-value criterion is almost universally used.
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