parametric tests (such as t or ANOVA) differ from nonparametric tests (such as c

Question

parametric tests (such as t or ANOVA) differ from nonparametric tests (such as chi-square) primarily in terms of the assumptions 1. Gravetter/Wallnau/Forzano, Essentials-Chapter 15- End-of-chapter question 1 Aa Aa Parametric tests (such as t or ANOVA) differ from nonparametric tests (such as chi-square) primarily in terms of the assumptions they require and the data they use. Which of the following are true about nonparametric tests? Check all that apply. Nonparametric tests require data measured on an interval or ratio scale. Nonparametric tests make few if any assumptions about the populations. Nonparametric tests require the populations to satisfy numerous assumptions. Nonparametric tests do not require the populations to have normal distributions. Nonparametric tests can be performed on data measured on any scale. Nonparametric tests require different populations to have equal variances.

Explanation / Answer

THE THINGS WHICH ARE TRUE ABOUT NON PARAMETRIC TESTS ARE:

1) NON PARAMETRIC TESTS DO NOT REQUIRE THE POPULATION TO HAVE NORMAL DISTRIBUTION.

2) NON PARAMETRIC TESTS CAN BE PERFORMED ON DATA MEASURED ON ANY SCALE.

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