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What does it mean when homogeneity of variance is significant?
Generally, tests of homogeneity of variance are tests on the deviations (squared or absolute) of scores from the sample mean or median. If the result of a Levene’s test is statistically significant, then the null hypothesis, that the groups have equal variances, is rejected.
How do you know if Levene’s test is significant?
Next, our sample sizes are sharply unequal so we really need to meet the homogeneity of variances assumption. However, Levene’s test is statistically significant because its p < 0.05: we reject its null hypothesis of equal population variances.
Why does Fligner Killeen indicate that variances are different?
For this reason I decided to use Fligner-Killeen test. The result for these two samples is: And indicates that variances are different. (Correct me if I’m wrong please). In the boxplot you can see that the variances are pretty similar, what could be the reason for why the test indicates that variances are different?
What is the syntax for the Fligner Killeen test?
The syntax for this function is given below: The Fligner-Killeen test is a non-parametric test for homogeneity of group variances based on ranks. It is useful when the data are non-normally distributed or when problems related to outliers in the dataset cannot be resolved.
Which is the best test for homogeneity of variance?
Bartlett’s test: A good first test for homogeneity of variance across groups. Levene’s test: More robust to departures from normality than the Bartlett’s test. Fligner-Killeen’s test: A non-parametric test for homogeneity of variance across groups.
Which is the best way to visualize homogeneity?
To illustrate ways to visualize homogeneity and compute the statistics, I will demonstrate with some golf data provided by ESPN. The golf data has 18 variables, you can see the first 10 below. Scatter plots are a useful way to look at the variance of a data and are, typically, our first step in assessing homogeneity.