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Psychology Notes On – Parametric And Non-Parametric Statistical Tests – For W.B.C.S. Examination.
Parametric Statistics
Parametric statistics are any statistical tests based on underlying assumptions about data’s distribution. In other words, parametric statistics are based on the parameters of the normal curve.Continue Reading Psychology Notes On – Parametric And Non-Parametric Statistical Tests – For W.B.C.S. Examination.
Because parametric statistics are based on the normal curve, data must meet certain assumptions, or parametric statistics cannot be calculated. Prior to running any parametric statistics, you should always be sure to test the assumptions for the tests that you are planning to run.
Nonparametric Statistics
As implied by the name, nonparametric statistics are not based on the parameters of the normal curve. Therefore, if your data violate the assumptions of a usual parametric and nonparametric statistics might better define the data, try running the nonparametric equivalent of the parametric test. You should also consider using nonparametric equivalent tests when you have limited sample sizes (e.g., n < 30). Though nonparametric statistical tests have more flexibility than do parametric statistical tests, nonparametric tests are not as robust; therefore, most statisticians recommend that when appropriate, parametric statistics are preferred.
Parametric tests assume underlying statistical distributions in the data. Therefore, several conditions of validity must be met so that the result of a parametric test is reliable. For example, Student’s t-test for two independent samples is reliable only if each sample follows a normal distribution and if sample variances are homogeneous.
Nonparametric tests do not rely on any distribution. They can thus be applied even if parametric conditions of validity are not met.
What is the advantage of using a nonparametric test?
Nonparametric tests are more robust than parametric tests. In other words, they are valid in a broader range of situations (fewer conditions of validity).
What is the advantage of using a parametric test?
The advantage of using a parametric test instead of a nonparametric equivalent is that the former will have more statistical power than the latter. In other words, a parametric test is more able to lead to a rejection of H0. Most of the time, the p-value associated to a parametric test will be lower than the p-value associated to a nonparametric equivalent that is run on the same data.
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