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Nonparametric Tests

Nonparametric Tests. Underlying population distribution is continuous. No other assumptions. Data need not be quantitative, but may be categorical or rank data. Very quick and easy to perform. Parametric vs. Nonparametric.

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Nonparametric Tests

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  1. Nonparametric Tests • Underlying population distribution is continuous. No other assumptions. • Data need not be quantitative, but may be categorical or rank data. • Very quick and easy to perform.

  2. Parametric vs. Nonparametric • When distribution is normal, parametric test is better. The nonparametric test requires larger sample size to achieve same power. • When distribution is not close to normal, nonparametric methods are much better. • Choose the parametric procedure whenever possible.

  3. + - + - Sign Test (1) • Tests hypotheses about the median. • Null hypothesis H0: • Find the differences xi – • Test statistic: R+ is number of differences that are positive. • What are the P-values for different tests? • Reject H0 if the P-value is less than a.

  4. + - + - Sign Test (2) • When n is large (at least 10), and p=0.5, the binomial is approximately normal. • R+ has a normal distribution with mean 0.5n and variance 0.25n. • The test statistic is:

  5. + - + - Sign Test for Paired Samples • Dj = X1j – X2j • The test statistic is:

  6. + - + - Type II Error • In determining b, we need: • The alternative median • AND the form of the underlying distribution

  7. + - + - Comparison to the t-Test • If underlying population is normal • Either test can be used • T-test has smallest b for all one-sided tests • T-test has smallest b for all two-sided tests with symmetric critical regions • If underlying population is nonnormal but symmetric • T-test will have smaller b unless tails are heavy • Sign test is considered test for median, rather than serious competitor for the t-test.

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