The power computation will give the proportion of experiments that reject the null hypothesis. The calculation is done using the F distribution with the ratio of the variances as parameter and the sample sizes- 1 as degrees of freedom.
Use the p-value to determine whether to reject or fail to reject the null hypothesis, which states that the population proportions in each category are consistent with the specified values in each category.
The p-value of all four normality tests are higher than 0.05 thus we cannot reject the null hypothesis that the variable from which the sample was extracted follows a Normal distribution.
The returned value of p indicates that kruskalwallis rejects the null hypothesis that all three data samples come from the same distribution at a 1% significance level.
Such misleading findings are often called a false positive, and in the world of statistics, are called a Type I error(if you incorrectly reject the null hypothesis that is actually true).
For this two-sided test we have to allocate the 5% chance of an error under the null hypothesis equally to both tails, and reject if the test statistic is too extreme in either direction.
In the test for equal proportions, which is the default for the CHISQ option, the null hypothesis specifies equal proportions of the total sample size for each class.
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