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If we use the test statistic /, then under the null hypothesis is exactly 1 for two-sided p-value, and exactly / for one-sided left-tail p-value, and same for one-sided right-tail p-value. If we consider every outcome that has equal or lower probability than "3 heads 3 tails" as "at least as extreme", then the p -value is exactly 1 / 2 ...
The exact binomial test gives p = 0.053 and McNemar's test with continuity correction gives = 3.68 and p = 0.055. The asymptotic McNemar's test gives = 4.55 and p = 0.033 and the mid-P McNemar's test gives p = 0.035. Both the McNemar's test and mid-P version provide stronger evidence for a statistically significant treatment effect in this ...
The p-value is below alpha = 0.05, so the null hypothesis that the observed and expected proportions are the same across all doses is rejected. The way to compute this is to get a cumulative distribution function for a right-tail chi-square distribution with 8 degrees of freedom, i.e. cdf_chisq_rt(x,8), or 1 − cdf_chisq_lt(x, 8).
The chi-squared test indicates a statistically significant association between the level of education completed and routine check-up attendance (chi2(3) = 14.6090, p = 0.002). The proportions suggest that as the level of education increases, so does the proportion of individuals attending routine check-ups.
In 2016, the American Statistical Association (ASA) published a statement on p-values, saying that "the widespread use of 'statistical significance' (generally interpreted as 'p ≤ 0.05') as a license for making a claim of a scientific finding (or implied truth) leads to considerable distortion of the scientific process". [57]
In statistics, Levene's test is an inferential statistic used to assess the equality of variances for a variable calculated for two or more groups. [1] This test is used because some common statistical procedures assume that variances of the populations from which different samples are drawn are equal. Levene's test assesses this assumption.