Normally you would find the critical value when given the p value and the test statistic.
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The critical value is used to test a null hypothesis against an alternative hypothesis at some pre-defined level of significance. A test statistic is calculated from the outcomes of a set of trials and if this test statistic is more extreme than the critical value then the null hypothesis must be rejected in favour of the alternative.
Every possible experimental outcome results in a value of the test statistic. The non-critical region is the collection of test statistic values that are associated with acceptance of the null hypothesis.
To start with you select your hypothesis and its opposite: the null and alternative hypotheses. You select a confidence level (alpha %), which is the probability that your testing procedure rejects the null hypothesis when, if fact, it is true.Next you select a test statistic and calculate its probability distribution under the two hypotheses. You then find the possible values of the test statistic which, if the null hypothesis were true, would only occur alpha % of the times. This is called the critical region.Carry out the trial and collect data. Calculate the value of the test statistic. If it lies in the critical region then you reject the null hypothesis and go with the alternative hypothesis. If the test statistic does not lie in the critical region then you have no evidence to reject the null hypothesis.
The answer depends on what the test statistic is: a t-statistic, z-score, chi square of something else.
The critical value for a 0.02 level of significance, denoted as α = 0.02, in a statistical test corresponds to the point on a distribution that separates the critical region (rejection region) from the non-critical region. To find the critical value, you would consult a statistical table or use a statistical calculator based on the specific test you are conducting (e.g., z-table, t-table, chi-square table). The critical value is chosen based on the desired level of significance, which represents the probability of rejecting the null hypothesis when it is actually true.