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Normality Tests

Normality Tests:

Normality tests are tests of whether a set of data is distributed in a way that is consistent with a normal distribution. Typically, they are tests of a null hypothesis that the data are drawn from a normal population, specifically a goodness-of-fit test. Hence, while it is possible to reach a definitive conclusion that a set of data is not normally-distributed (by rejecting the null hypothesis), the most one can say if the null hypothesis is not rejected is that the data could possibly come from a normally distributed population. See Lilliefors test for normality.

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Courses Using This Term

Clinical Trials – Pharmacokinetics and Bioequivalence
This course will teach you the statistical measurement and analysis methods relevant to the study of pharmacokinetics, dose-response modeling, and bioequivalence. The course provides practical work with actual/simulated clinical trial data.
Multivariate Statistics
This course will teach you key multivariate procedures such as multivariate analysis of variance (MANOVA), principal components, factor analysis, and classification.
Sample Size and Power Determination
This course will teach you how to make sample size determinations for various statistical tests and for confidence intervals, as needed for experimental studies such as comparison studies, as well as for other types of experiments.
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