- 6 Videos
- 30 Exercises
- 3 hours
- 29,500 Participants
- 1950 XP

**Instructor(s):**

Andrew Conway is a Psychology Professor in the Division of Behavioral and Organizational Sciences at Claremont Graduate University in Claremont, California. He has been teaching introduction to statistics for undergraduate students and advanced statistics for graduate students for 20 years, at a variety of institutions, including the University of South Carolina, the University of Illinois in Chicago, and Princeton University.

If you want to have a solid basic foundation in statistics, it is essential to understand the concepts and theories behind t-tests. This module covers both the intuition and the calculations behind dependent t-tests, independent t-tests and z-scores. Topics such as null hypothesis significance testing (NHST), p-values, and effect size are covered in detail.

The first part covers z-tests, single sample t-tests, and dependent t-tests. You will learn when to use a z-test, when to use a t-test, and how you can calculate the corresponding test statistic. The focus is on understanding how t-tests are constructed, the intuition and interpretation behind them, and how R can help you to do t-tests more easily.

- Statistical tests to compare means 50 xp
- Polling liberals and conservatives 50 xp
- More political polls 50 xp
- Sampling distributions (1) 50 xp
- Significance tests 50 xp
- What's a summary statistic? 50 xp
- Sampling distributions (2) 50 xp
- Single sample t-tests 50 xp
- Understanding the t-distribution 100 xp
- Dependent t-tests 50 xp
- The working memory dataset 100 xp
- Perform a dependent t-test 100 xp
- Perform a dependent t-test (2) 100 xp
- Compare the observed and critical t-values 50 xp
- Interpreting the effect size 50 xp
- Let R do the dirty work 100 xp
- Pop quiz! 50 xp

The independent t-test is one of the most common statistical test that you will encounter. An independent t-test is appropriate when you want to compare two independent samples, so two completely different groups. Common examples are comparisons between men and women, or treatment group vs control group. The example used to explain the theory behind independent t-tests is the working memory training example.