# Interpret the key results for Friedman Test

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.

If your data have ties, Minitab displays a p-value that is adjusted for ties and a p-value that is not adjusted for ties. A tie occurs when the same value is in more than one sample. The adjusted p-value is usually more accurate than the unadjusted p-value. However, because the unadjusted p-value is always greater than the adjusted p-value, it is considered the more conservative estimate. When no ties exist in your data, the two p-values are equal.