Thank you in advance. Dave, Yes, this is what the statistical power analysis does. Type II error is equal to one minus the statistical power. But I noticed the variance in one group was twice as high as the other, so I decided to try the unequal variance t-test, and got the same significant results.
What would cause this difference? Does excel remove observations according to some sort of rule in the unequal variance test? THis should no happen. It is not surprising that the df changes since this is the main difference between the equal variances and unequal variances version of the t-test. With such large samples, the equal variances t-test is pretty robust even when the variance of one sample is two or three times the variance of the other sample.
Hi, When I tested with unequal variances i got significant results but my t-stat value is 3. Why I am getting results like that. Ramya, Are you saying that your test results in a high t-statistic and a low p-value for a significant result? This just means that you have evidence that the means of the corresponding populations are likely to be different. Hello I used t-test for two tailed unequal variances in excel and I obtained t stat greater than critical t value, however my p-value is 3. I do not know whether I have to say there is a significant difference or reject that because p-value is greater than 0.
Another issue is when I use T. I do not why? I am appreciated for your guidance. Even though sample means are the same, but if the variance is different, what does it tell us? Case 1: two different medical procedure was applied on the same group of patients. How to test if two procedures are significantly different from each other? Case 2: one class taught by the same teacher is split into two groups and take exams. But the supervisor who has the exam result doesn't know about this.
He wants to know if the two groups samples came from the same classroom population. What does unequal variance test do here? I also read that F-test is used to test difference in variance. How does F-test relate to unequal, or equal variance test? The alternative to the Welch 2-sample t test is the pooled 2-sample t test.
In order for the pooled test to give reliable results, it is necessary for population variances to be equal. But the Welch test works well--whether or not the variances are equal. Pooled t test. So I'll falsely believe means are unequal when they really are equal. As a result, I might publish some false "discoveries. Welch t test. What's the point?
In conclusion, the point of using the Welch test is that performs well even if population variances are not equal. In practice, one usually doesn't know whether or not population variances are equal. So good statistical practice is to use the Welch version of the two-sample t test, unless one has reliable prior evidence that population variances are equal. Note: The F-test for unequal variances has poor power. It should not be used to 'screen' whether to use the pooled or the Welch test.
If there is any uncertainty about unequal variances, automatically use the Welch test. But they differ in the assumption about the variances of the population: only the Student's test require them to be equal. Sign up to join this community. For the unequal variance t test, the null hypothesis is that the two population means are the same but the two population variances may differ.
If the P value is large, you don't reject that null hypothesis, so conclude that the evidence does not persuade you that the two population means are different, even though you assume the two populations have or may have different standard deviations. What a strange set of assumptions. What would it mean for two populations to have the same mean but different standard deviations?
Why would you want to test for that? Swailowsky points out that this situation simply doesn't often come up in science 1. I think the unequal variance t test is more useful when you think about it as a way to create a confidence interval. Your prime goal is not to ask whether two populations differ, but to quantify how far apart the two means are. The unequal variance t test reports a confidence interval for the difference between two means that is usable even if the standard deviations differ.
Both t tests report both a P value and confidence interval. The calculations differ in two ways:. It seems sensible to first test whether the variances are different, and then choose the ordinary or Welch t test accordingly. In fact, this is not a good plan. You should decide to use this test as part of the experimental planning.
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