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Which type of statistical test is applicable for paired samples?
The paired sample t-test, sometimes called the dependent sample t-test, is a statistical procedure used to determine whether the mean difference between two sets of observations is zero. In a paired sample t-test, each subject or entity is measured twice, resulting in pairs of observations.
Can you do a paired t-test with categorical data?
This is the equivalent of the paired samples t-test, but allows for two or more levels of the categorical variable. This tests whether the mean of the dependent variable differs by the categorical variable. We have an example data set called rb4, which is used in Kirk’s book Experimental Design.
Which is the best test for paired data?
I turned to the paired t-test, which requires normality of difference between the two variables. But is it even possible to use this when the data is discrete? The histogram (with normal distribution plotted on top) and a qqplot can be seen in attached figure.
Which is the best test for discrete data?
Your data is clearly non-normal, taking discrete values only, but the sample mean will be fairly Normally distributed on account of your large sample size. If your sample size was much smaller then the Wilcoxon signed-rank test would be more suitable, avoiding the Normal assumption.
Which is an alternative hypothesis for discrete outcome?
The alternative or research hypothesis is that there is a difference in the distribution of responses to the outcome variable among the comparison groups (i.e., that the distribution of responses “depends” on the group). In order to test the hypothesis, we measure the discrete outcome variable in each participant in each comparison group.
Why does the t-test work on discrete data?
Just to expand on why the t-test is working: this is a result of the Central Limit Theorem, which tells us that the sample mean has a Normal distribution as n grows. Your data is clearly non-normal, taking discrete values only, but the sample mean will be fairly Normally distributed on account of your large sample size.