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What is the Lilliefors correction?
The Lilliefors correction has been employed in the Explore procedure (EXAMINE command) to correct the significance value for use of the sample mean and SD in place of a hypothesized population mean and SD.
What does Lilliefors test?
In statistics, the Lilliefors test is a normality test based on the Kolmogorov–Smirnov test. A variant of the test can be used to test the null hypothesis that data come from an exponentially distributed population, when the null hypothesis does not specify which exponential distribution.
When to use Lilliefors test?
Unlike the K-S test, Lilliefors can be used when you don’t know the population mean or standard deviation. Essentially, the Lilliefors test is a K-S test that allows you to estimate these parameters from your sample.
Which is an example of the Lilliefors test?
Example 1: Repeat Examples 1 and 2 of the Kolmogorov-Smirnov Test for Normality using the Lilliefors test. For Example 1 of Kolmogorov-Smirnov Test for Normality, using the Lilliefors Test Table, we have
What are the results of the Lilliefors normality test?
Lilliefors (Kolmogorov-Smirnov) normality test data: moist D = 0.191, p-value = 0.2628 If you like our critical approach to analysis you will really like our hyperbook: Avoiding and Detecting Statistical Malpractice (Design & Analysis for Biologists, with R).
How is the Lilliefors test different from Kolmogorov-Smirnov?
The Lilliefors test uses the same calculations as the Kolmogorov-Smirnov test, but the table of critical values in the Lilliefors Test Table is used instead of the Kolmogorov-Smirnov Table. Since the critical values in this table are smaller, the Lilliefors Test is less likely to show that data is normally distributed.
Which is the maximal absolute difference in the Lillie test?
The Lilliefors (Kolmogorov-Smirnov) test is an EDF omnibus test for the composite hypothesis of normality. The test statistic is the maximal absolute difference between empirical and hypothetical cumulative distribution function.