# Interpret the key results for Mood’s Median Test

Complete the following steps to interpret a Mood's median test. Key results include the median estimates, the p-value, and the confidence intervals.

## Step 1: Determine whether the differences between group medians are statistically significant

To determine whether any of the differences between the medians are statistically significant, compare the p-value to your significance level to assess the null hypothesis. The null hypothesis states that the population medians are all equal. Usually, a significance level (denoted as α or alpha) of 0.05 works well. A significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference.
P-value ≤ α: The differences between some of the medians are statistically significant
If the p-value is less than or equal to the significance level, you reject the null hypothesis and conclude that not all the population medians are equal. Use your specialized knowledge to determine whether the differences are practically significant. For more information, go to Statistical and practical significance.
P-value > α: The differences between the medians are not statistically significant
If the p-value is greater than the significance level, you do not have enough evidence to reject the null hypothesis that the population medians are all equal. Verify that your test has enough power to detect a difference that is practically significant. For more information, go to Increase the power of a hypothesis test.

## Step 2: Assess the estimate of the population median for each group

Use the confidence intervals (95% Median CI) to assess the estimate of the population median for each group. The confidence intervals are ranges of values that are likely to contain the population median.

For example, with a 95% confidence level, you can be 95% confident that the confidence interval contains the group median. The confidence interval helps you assess the practical significance of your results. Use your specialized knowledge to determine whether the confidence interval includes values that have practical significance for your situation. If the interval is too wide to be useful, consider increasing your sample size.

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