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Warning pessimistic/cynical post We do not want to move away from significance. That is a false premises that lead to your question. Recently I have found that many statisticians are speaking of moving away from significance. .. Since we want to move away from significance... We do not want to move away from significance. Significance is important. It is ...


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This is too long to be a comment, but just to add to the excellent answer by Sextus, one issue with "significance" is the arbitrary nature of the significance level(s). Often these are dictated by whatever is common practice in a particular field. Also, when a researcher performs a test and finds a p-value of, say 0.0499999 they may claim to have ...


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Neither The Mann-Whitney(-Wilcoxon) $U$ test is typically a test of $\text{H}_{0}\text{: }P(X_{A} > X_{B}) = 0.5$, rejected in favor of $\text{H}_{\text{A}}\text{: }P(X_{A} > X_{B}) \ne 0.5$. In plain language: the probability that a randomly selected observation from group $\text{A}$ is greater than a randomly selected observation from group $\text{B}$...


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The Wilcoxon Mann-Whitney two-sample rank sum test tests whether observations from one group tend to be bigger than observations from another group. It is used for ordinal or continuous response variables Y and not for the case where Y is binary or represents unordered categories. But if Y were binary the p-value from the Wilcoxon test, though not very ...


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The false discovery rate (FDR) is the expected proportion of type I errors, which is where you incorrectly reject the null hypothesis. α is the p-value you choose to use. You have defined it as 0.05, which is a common choice. At this p-value, there's a 5% chance of a Type 1 error.


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The result shows only two coefficients for education because glm converts factors (e.g., your education variable) into indicator variables for each level. Since having all theee indicators would be redundant (because if you know the education is not medium or high, it must be low) you only see two, with the third being implicit. When you write y = -2.35 + 0....


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It is informative to see exactly what the Mann-Whitney test does. For two samples $X = \{x_1, \dots, x_m \}$ and $Y=\{y_1, \dots, y_n\}$, under the assumptions that Observations in $X$ are iid Observations in $Y$ are iid The samples $X$ and $Y$ are mutually independent. The respective populations from which $X$ and $Y$ were sampled are continuous. then, ...


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If your joint null hypothesis that happines is equal over all four times of day (i.e., over all four bins), and you do not want to make distributional assumptions about the happiness variable, then a Kruskal Wallis test kruskal.test() would be a test of choice to test exactly that. It is a generealization of the Wilcoxon/Mann-Whitney-U test for more than two ...


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Sporty question. Why not use a binomial test for this purpose? You want to test whether your $H_1: P > 0.70$ as opposed to the opposite $H_0: P \leq 0.70$ The binomial test reasons on a fraction in a distribution, and it is exact. Reference S. Siegel, N.J. Castellan. Nonparametric statistics for the behavirioral sciences, McGraw-Hill, 1988, chapter 4.


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