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How is the t test a parametric test?
The t-test is a parametric test of difference, meaning that it makes the same assumptions about your data as other parametric tests. The t-test assumes your data: The t-test assumes your data: are independent
When to use CLT in a t test?
By contrast, about 75% of samples of size 30 from an exponential distribution have outliers in a boxplot and most have their upper whisker longer than the lower one, indicating upward skewness. So data frequently show warning signs when t tests are inappropriate.
How does the two sample t test work?
The two-sample t-test allows us to test the null hypothesis that the population means of two groups are equal, based on samples from each of the two groups. In its simplest form, it assumes that in the population, the variable/quantity of interest X follows a normal distribution in the first group and is in the second group.
What happens if the t-test assumes normality?
Of course if X isn’t normally distributed, even if the type 1 error rate for the t-test assuming normality is close to 5%, the test will not be optimally powerful. That is, there will exist alternative tests of the null hypothesis which have greater power to detect alternative hypotheses.
When to use a paired or two sample t test?
If you are studying one group, use a paired t-test to compare the group mean over time or after an intervention, or use a one-sample t-test to compare the group mean to a standard value. If you are studying two groups, use a two-sample t-test. If you want to know only whether a difference exists, use a two-tailed test.
What to consider when choosing a t test?
When choosing a t-test, you will need to consider two things: whether the groups being compared come from a single population or two different populations, and whether you want to test the difference in a specific direction. One-sample, two-sample, or paired t-test?
How is the t value of a t test calculated?
A t-test measures the difference in group means divided by the pooled standard error of the two group means. In this way, it calculates a number (the t-value) illustrating the magnitude of the difference between the two group means being compared, and estimates the likelihood that this difference exists purely by chance (p-value).