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Intro to Statistics with R: Student's T-test

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  • 6 Videos
  • 30 Exercises
  • 3 hours 
  • 29,407 Participants
  • 1950 XP

Instructor(s):

Andrew Conway
Andrew Conway

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.

Course Description

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.

Independent t-tests 

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.

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