PSYC 2530 Chapter Notes - Chapter 12: Repeated Measures Design, Statistical Hypothesis Testing, Type I And Type Ii Errors

• School: York University
• Department: Psychology
• Course: PSYC 2530
• Professor: Anne Russon

Document Summary

12. 1 | introduction (an overview of analysis of variance) Analysis of variance (anova) is a hypothesis-testing procedure that is used to evaluate mean differences between two or more treatments (or populations) Anova uses sample data as the basis for drawing general conclusions about populations. Its advantage over t-tests is that it can be used to compare two or more treatments. Thus, anova provides much greater flexibility in designing experiments and interpreting results. The goal of the analysis is to determine whether the mean differences observed among the samples provide enough evidence to conclude that there are mean differences among the populations. Specifically, we must decide between two interpretations: 01. There really are no differences between the populations (or treatments). The observed differences between the sample means are caused by random, unsystematic factors (sampling error) that differentiate one sample from another: 02.