Why is it necessary to assume equal variances in two sample t tests?

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Homework Help Overview

The discussion revolves around the necessity of assuming equal variances in two-sample t-tests, particularly in the context of statistical testing and the implications of the Central Limit Theorem.

Discussion Character

  • Conceptual clarification, Assumption checking

Approaches and Questions Raised

  • Participants explore the reasoning behind the assumption of equal variances, questioning its necessity and implications for the validity of the t-test. Some discuss the application of the Central Limit Theorem and its limitations with small sample sizes.

Discussion Status

There is an ongoing exploration of the conditions under which different statistical tests should be applied, with some participants clarifying the distinction between two-sample and paired-sample tests. Guidance has been offered regarding the necessity of equal variances in specific contexts.

Contextual Notes

Participants are considering scenarios involving small and large sample sizes, as well as known versus unknown variances, which may influence the choice of statistical test.

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Homework Statement


My question is why is the assumption necessary to make? (Please see the image).

Homework Equations

The Attempt at a Solution


We can easily proceed by treating the two samples as two different population, find their individual unbiased estimate of variance and then use the combined estimate to apply the Central Limit Theroem. So why assume the variances of both samples is equal?
 

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Faiq said:

Homework Statement


My question is why is the assumption necessary to make? (Please see the image).

Homework Equations

The Attempt at a Solution


We can easily proceed by treating the two samples as two different population, find their individual unbiased estimate of variance and then use the combined estimate to apply the Central Limit Theroem. So why assume the variances of both samples is equal?

The Central Limit Theorem applies in the limit of infinite sample slzes and gives a reasonable approximation for very large but finite samples. However, for sample sizes like in the question you cite, the central limit theorem does not apply very well, if at all, so you need to use the t-distribution instead of the normal. For a two-sample t-test you need equality of variances just because without it the test statistic does not follow the t-distribution. It is just a requirement of the mathematics.
 
Oh so equality of variances is necessary for any two sample t or z test ?
 
Faiq said:
Oh so equality of variances is necessary for any two sample t or z test ?

Yes, that is what I said.

However, in a paired-sample t-test you do not need equality of variances, so be careful to make the distinction between"two-sample" and "paired-sample" (both of which look at two samples, but in different ways).
 
Thank you very much
 
Can you please tell me what test am I suppose to use in these situations (2 sample z test or 2 sample t test)?
1. Two sample, sample size small, variance of both known and is different
2. Two sample, sample size large, variance of both unknown

I am asking because you said when sample size is small, we have to use t-test for which variance must be equal. In that case I can't identify the type of test to be used in the abovementioned conditions.
 

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