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Non-parametric Tests

Key Terms . Power of a test refers to the probability of rejecting a false null hypothesis (or detect a relationship when it exists)Power Efficiency the power of the test relative to that of its most powerful alternative. For example, if the power efficiency of a certain nonparametric test for dif

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Non-parametric Tests

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    1. Non-parametric Tests Research II MSW PT Class 8

    2. Key Terms Power of a test refers to the probability of rejecting a false null hypothesis (or detect a relationship when it exists) Power Efficiency the power of the test relative to that of its most powerful alternative.  For example, if the power efficiency of a certain nonparametric test for difference of means with sample size 10 is 0.9, it means that if interval scale and the normality assumptions can be made (more powerful), we can use the t-test with a sample size of 9 to achieve the same power.

    3. Choice of nonparametric test It depends on the level of measurement obtained (nominal, ordinal, or interval), the power of the test, whether samples are related or independent, number of samples, availability of software support (e.g. SPSS) Related samples are usually referred to match-pair (using randomization) samples or before-after samples.   Other cases are usually treated as independent samples.  For instance, in a survey using random sampling, we have a sub-sample of males and a sub-sample of females.  They can be considered as independent samples as they are all randomly selected.

    5. One-sample case Binomial – tests whether the observed distribution of dichotomous variable (a variable that has two values only) is the same as that expected from a given binomial distribution.  The default value of p is 0.5.  You can change the value of p.  For example, a couple has given birth consecutively 8 baby girls, and you would like to test if their probability of given birth to baby girls is > 0.6 or >0.7, you can test the hypothesis by changing the default value of p in the SPSS programme.

    9. Chi-square – tests whether the observed distribution is the same as a certain hypothesized distribution.  The default null hypothesis is even distribution. 

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