![]() ![]() ![]() The test statistic of the Mood’s median test is actually based on another well known hypothesis test: the chi-square test. Handpicked Content: Hypothesis Testing: Fear No More How the Mood’s Median Test Works Figure 4: Mood’s Median Test: Quality Control Analysis Time Versus ProductĬhi-square = 8.27 Degrees of freedom (DF) = 2 p = 0.016 ![]() In this example, at least product A and C have significantly different analysis times (Figure 4). The rule is: If there is no overlap between the confidence intervals, a significant difference can be assumed. The 95 percent confidence intervals of the individual group medians now help to find where the significant difference is. For the QC analysis time, the p- value is 0.016 – in other words, less than 0.05. If the p-value is less than the agreed Alpha risk of 5 percent (0.05), the null hypothesis is rejected and at least one significant difference can be assumed. If the assumptions are met, the Mood’s median test can be conducted. Comparing the medians of the number of calls per week ( Y) at a service hotline separated by four different call types ( X = complaint, technical question, positive feedback or product info) over the last six monthsįigure 3: Lognormality Test of Quality Control Analysis Time.Comparing the medians of the monthly satisfaction ratings ( Y) of six customers ( X) over the last two years.Comparing the medians of manufacturing cycle time ( Y) of three different production lines ( X = Lines A, B and C).When to Use Mood’s Median TestĮxamples for the usage of the Mood’s median test include: The Mood’s median test works when the Y variable is continuous, discrete-ordinal or discrete-count, and the X variable is discrete with two or more attributes. Therefore, it provides a nonparametric alternative to the one-way ANOVA. The Mood’s median test is a nonparametric test that is used to test the equality of medians from two or more populations. If assumptions do not hold, nonparametric tests are a better safeguard against drawing wrong conclusions. Nonparametric tests do not make assumptions about a specific distribution. That is why hypothesis tests such as the t-test, paired t-test and analysis of variance (ANOVA) are also called parametric tests. When comparing the average of two or more groups with the help of hypothesis tests, the assumption is that the data is a sample from a normally distributed population. ![]()
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