Basically, it comes down to whether the inference is going to contain claims regarding the direction of the effect or not. For the F statistic there are two separate degrees of freedom - one for the numerator and one for the denominator.įinally, to determine a critical region, one needs to know whether they are testing a point null versus a composite alternative (on both sides) or a composite null versus (covering one side of the distribution) a composite alternative (covering the other). Then, for distributions other than the normal one (Z), you need to know the degrees of freedom. F-distributed (Fisher-Snedecor distribution), usually used in analysis of variance (ANOVA).X 2-distributed ( Chi square distribution, often used in goodness-of-fit tests, but also for tests of homogeneity or independence).T-distributed (Student's T distribution, usually appropriate for small sample sizes, equivalent to the normal for sample sizes over 30).Z-distributed (normally distributed, e.g.Our critical value calculator supports statistics which are either: Then you need to know the shape of the error distribution of the statistic of interest (not to be mistaken with the distribution of the underlying data!). For example, 95% significance results in a probability of 100%-95% = 5% = 0.05. If you know the significance level in percentages, simply subtract it from 100%. You need to know the desired error probability ( p-value threshold, common values are 0.05, 0.01, 0.001) corresponding to the significance level of the test. significance test, statistical significance test), determining the value of the test statistic corresponding to the desired significance level is necessary. If you want to perform a statistical test of significance (a.k.a.
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