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Is t-test robust to normality?
the t-test is robust against non-normality; this test is in doubt only when there can be serious outliers (long-tailed distributions – note the finite variance assumption); or when sample sizes are small and distributions are far from normal. 10 / 20 Page 20 . . .
Is independent t-test robust to violations of normality?
Assumption of normality of the dependent variable Note: Technically, it is the residuals that need to be normally distributed, but for an independent t-test, both will give you the same result. However, the t-test is described as a robust test with respect to the assumption of normality.
What are the assumptions for a two-sample independent t-test?
Two-sample t-test assumptions Data in each group must be obtained via a random sample from the population. Data in each group are normally distributed. Data values are continuous. The variances for the two independent groups are equal.
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 is the t-test ” reasonably robust “?
I’ve read that the t -test is “reasonably robust” when the distributions of the samples depart from normality. Of course, it’s the sampling distribution of the differences that are important. I have data for two groups. One of the groups is highly skewed on the dependent variable.
Which is the most important assumption in a t test?
Assumption 2: Normality A two sample t-test makes the assumption that both samples are approximately normally distributed. This is a crucial assumption because if the samples are not normally distributed then it isn’t valid to use the p-values from the test to draw conclusions about the differences between the samples.
Is the two sample t-test still unbiased?
Overall, the two sample t-test is reasonably power-robust to symmetric non-normality (the true type-I-error-rate is affected somewhat by kurtosis, the power is impacted mostly by that). When the two samples are mildly skew in the same direction, the one-tailed t-test is no longer unbiased.