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Can you do a paired t test with 3 groups?
The trouble with not doing pairwise t-tests is that you have no details about the components of variance contributed by each group. for comparing three means you can use Both ANOVA and t test. t test is mainly used to compare two group means. for comparing more than two group means ANOVA is used.
Can you do a paired t test with more than 2 groups?
Note: The Paired Samples t Test can only compare the means for two (and only two) related (paired) units on a continuous outcome that is normally distributed. To compare unpaired means between more than two groups on a continuous outcome that is normally distributed, choose ANOVA.
What is a paired t test for more groups?
A paired t-test (also known as a dependent or correlated t-test) is a statistical test that compares the averages/means and standard deviations of two related groups to determine if there is a significant difference between the two groups.
How to perform paired t test for multiple groups?
Here is the scenario: I would like to perform a paired t-test of sorts on multiple groups. Instead of before and after conditions, there are multiple conditions (ex: treatment 1, treatment 2, treatment 3 etc). Each subject appears in each ‘group’.
What is the formula for a paired sample t test?
Paired Samples t-test: Formula. A paired samples t-test always uses the following null hypothesis: H 0: μ 1 = μ 2 (the two population means are equal) The alternative hypothesis can be either two-tailed, left-tailed, or right-tailed: H 1 (two-tailed): μ 1 ≠ μ 2 (the two population means are not equal)
What’s the significance level for a paired t-test?
In statistics-speak, we set the significance level, denoted by α, to 0.05. It’s a good practice to make this decision before collecting the data and before calculating test statistics. We calculate a test statistic. Our test statistic is 0.750. We find the value from the t -distribution.
Is it good practice to do multiple t tests?
There would be no harm in that because a significant F value would indicate that at least two of your three groups differ from each other (possibly / probably Groups 1 and 2 and/or 1 and 3). Furthermore, as pointed out above, it’s not good practice to do multiple t -tests because of the risk of committing Type 1 errors.