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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.
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.
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.
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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