PSYC 2530 Chapter Notes - Chapter 17: Chi-Squared Test, Chi-Squared Distribution, Null Hypothesis
28 Nov 2016
School
Department
Course
Professor
Document Summary
17. 1 | introduction to chi-square: the test for goodness of fit. For anova, the population distributions are assumed to be normal and homogeneity of variance is required. Because these tests all concern parameters and require assumptions about parameters, they are called parametric tests. There are several hypothesis-testing techniques that provide alternatives to parametric tests. Two chi-square tests do not state the hypotheses in terms of a specific parameter and they make few assumptions about the population distribution. Therefore, nonparametric tests sometimes are called distribution-free tests. For nonparametric tests, the participants are usually just classified into categories. These classifications involve measurement on nominal or ordinal scales, and they do not produce numerical values that can be used to calculate means and variances. Instead, the data for many nonparametric tests are simply frequencies. There are situations for which transforming scores into categories might be a better choice: 01. It is simpler to obtain category measurements: 02.
