Data Analysis for the Behavioral Sciences

Can you think of an area of your life that is influenced by statistics? Many times when we think about statistics in our daily lives, we think about numerical expressions of statistics, such as the number of daily COVID cases in our county, the percentage of students admitted each year to our university, or the number of people that voted in the last election. From each of these examples, we could go on to make inferences or look to answer questions based on this data, such as whether to open restaurants, how many new students are psychology majors, or if a specific issue drove voters to the polls in a specific state.

This course will begin by introducing the basic concepts of how to describe and visualize data, the fundamentals of using statistics to make inferences, and the logic of null hypothesis testing. Various types of hypothesis tests will be introduced, along with criteria for selecting which is appropriate for different study conditions. As an extension of null hypothesis significance tests, you will learn about how to interpret effect sizes and confidence intervals, along with statistical power, before being introduced to alternatives to null hypothesis significance testing. All this is fleshed out in Data Analysis for the Behavioral Sciences.

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