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In null-hypothesis significance testing, the p-value [note 1] is the probability of obtaining test results at least as extreme as the result actually observed, under the assumption that the null hypothesis is correct. [2] [3] A very small p-value means that such an extreme observed outcome would be very unlikely under the null hypothesis.
Similar to standard hypothesis testing, the p-value together with a threshold (referred to as significance level in the CP field) is used to determine whether the label should be in the prediction set. For example, for a significance level of 0.1, all classes with a p-value of 0.1 or greater are added to the prediction set. Transductive ...
A desired significance level α would then define a corresponding "rejection region" (bounded by certain "critical values"), a set of values t is unlikely to take if was correct. If we reject H 0 {\displaystyle H_{0}} in favor of H 1 {\displaystyle H_{1}} only when the sample t takes those values, we would be able to keep the probability of ...
For a given significance level in a two-tailed test for a test statistic, the corresponding one-tailed tests for the same test statistic will be considered either twice as significant (half the p-value) if the data is in the direction specified by the test, or not significant at all (p-value above ) if the data is in the direction opposite of ...
More precisely, a study's defined significance level, denoted by , is the probability of the study rejecting the null hypothesis, given that the null hypothesis is true; [4] and the p-value of a result, , is the probability of obtaining a result at least as extreme, given that the null hypothesis is true. [5]
[13] [14] [15] The apparent contradiction stems from the combination of a discrete statistic with fixed significance levels. [16] [17] Consider the following proposal for a significance test at the 5%-level: reject the null hypothesis for each table to which Fisher's test assigns a p-value equal to or smaller than 5%. Because the set of all ...
The statistical significance of each B is tested by the Wald Chi-Square—testing the null that the B coefficient = 0 (the alternate hypothesis is that it does not = 0). p-values lower than alpha are significant, leading to rejection of the null. Here, only the independent variables felony, rehab, employment, are significant ( P-Value<0.05.
This means that the p-value is a statement about the relation of the data to that hypothesis. [2] The 0.05 significance level is merely a convention. [3] [5] The 0.05 significance level (alpha level) is often used as the boundary between a statistically significant and a statistically non-significant p-value. However, this does not imply that ...