Search results
Results From The WOW.Com Content Network
p. -value. In null-hypothesis significance testing, the -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.
If one's F-statistic is greater in magnitude than their critical value, we can say there is statistical significance at the 0.05 alpha level. The F-test is used for comparing the factors of the total deviation. For example, in one-way, or single-factor ANOVA, statistical significance is tested for by comparing the F test statistic
The omnibus F ANOVA test results above indicate significant differences between the days time-wait (P-Value =0.000 < 0.05, α =0.05). The other omnibus tested was the assumption of Equality of Variances, tested by the Levene F test:
The critical value is the number that the test statistic must exceed to reject the test. In this case, F crit (2,15) = 3.68 at α = 0.05. Since F=9.3 > 3.68, the results are significant at the 5% significance level. One would not accept the null hypothesis, concluding that there is strong evidence that the expected values in the three groups ...
False discovery rate. In statistics, the false discovery rate (FDR) is a method of conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons. FDR-controlling procedures are designed to control the FDR, which is the expected proportion of "discoveries" (rejected null hypotheses) that are false ...
If the resulting p-value of Levene's test is less than some significance level (typically 0.05), the obtained differences in sample variances are unlikely to have occurred based on random sampling from a population with equal variances. Thus, the null hypothesis of equal variances is rejected and it is concluded that there is a difference ...
The Kruskal–Wallis test' by ranks, Kruskal–Wallis test (named after William Kruskal and W. Allen Wallis), or one-way ANOVA on ranks is a non-parametric statistical test for testing whether samples originate from the same distribution. [ 1 ][ 2 ][ 3 ] It is used for comparing two or more independent samples of equal or different sample sizes.
The p-value is the probability that a test statistic which is at least as extreme as the one obtained would occur under the null hypothesis. At a significance level of 0.05, a fair coin would be expected to (incorrectly) reject the null hypothesis (that it is fair) in 1 out of 20 tests on average.