In these results, using the default (1 – alpha) x 100% method and an alpha of 0.05 produces a 95% CI for equivalence of (0, 2.1928). Like a standard confidence interval, the confidence interval for equivalence is calculated using information about the point estimate of the difference (or ratio), the sample size, and the variability in the data.

To determine whether the difference between the population means is statistically significant, compare the p-value to the significance level. Usually, a significance level (denoted as α or alpha) of 0.05 works well.

To determine whether the order of your data is random, compare the p-value to the significance level. Usually, a significance level (denoted as α or alpha) of 0.05 works well.

To determine whether the data follow the distribution, compare the p-value to the significance level. Usually, a significance level (denoted as α or alpha) of 0.05 works well.

To determine whether the difference between the medians is statistically significant, compare the p-value to the significance level. Usually, a significance level (denoted as α or alpha) of 0.05 works well.

To determine whether an outlier exists, compare the p-value to the significance level. Usually, a significance level (denoted as α or alpha) of 0.05 works well.

Suppose that you do a hypothesis test. Remember that the decision to reject the null hypothesis (H 0) or fail to reject it can be based on the p-value and your chosen significance level (also called α).If the p-value is less than or equal to α, you reject H …

Compare the p-value for each distribution or transformation to the significance level. Usually, a significance level (denoted as α or alpha) of 0.05 works well.

To determine whether the data do not follow a normal distribution, compare the p-value to the significance level. Usually, a significance level (denoted as α or alpha) of 0.05 works well.

Key Results: P-Value. In these results, the p-value is 0.340, which is greater than the significance level of 0.05. Because you can assume that your data follow a normal distribution, you can use the normal method tolerance interval.