• Explain various ways to categorize variables.

• Explain various ways to describe data.

• Describe how graphs are used to visualize data.

• Explain the meaning of a correlation coefficient.

• Describe the logic of inferential statistics.

• Explain the logic of null hypothesis significance testing.

• Select the appropriate inferential test based on study criteria.

• Compare and contrast the use of statistical significance, effect size, and confidence intervals.

• Explain the importance of statistical power.

• Describe how alternative procedures address the major objections to null hypothesis significance testing.

Data Analysis for the Behavioral Sciences

Learning Plan

Data Analysis Basics

Variables and Measures

Describing Data

Section Summary

Null Hypothesis Significance Testing

Inferential Statistics

Null Hypothesis Significance Testing

The Variety of Null Hypothesis Significance Tests

Beyond Null Hypothesis Significance Testing

Preview

The “New Statistics”

Statistical Power

Alternatives to Null Hypothesis Significance Testing

Course Summary

Explain various ways to categorize variables

Explain various ways to describe data

Explain the meaning of a correlation coefficient

Describe the logic of inferential statistics

Explain the logic of null hypothesis significance testing

Select the appropriate inferential test based on study criteria

Compare and contrast the use of statistical significance, effect size, and confidence intervals

Explain the importance of statistical power