# Thread: Independent t-test - interpreting significance if sizes of samples differ greatly

1. ## Independent t-test - interpreting significance if sizes of samples differ greatly

Hello,

I ran an independent t-test on two samples (composite scores of a health assessment for two different groups of youth by age category).

Group 1: N=1,296, Mean=17.47, STDEV=3.49
Group 2: N=628, Mean=19.49, STDEV=3.93

Levene's Test significant at p=.000. Therefore, equality of means NOT assumed.
Reading that line (equality of means not assumed), I see: T=-10.92, 2-tailed sig @ p =.000. Mean diff=-2.01, 95% CI = -2.38 to -1.65.

Can I use these results or are they invalid because my sample sizes are vastly different (one is twice the size of the other)?

Thank you,

2. ## Re: Independent t-test - interpreting significance if sizes of samples differ greatly

Can I use these results or are they invalid because my sample sizes are vastly different (one is twice the size of the other)?
If unequal variances are associated with unequal sample sizes, then problems can arise. The software has already corrected the calculations, though (Welch test). Your results can be trusted, AFAICS.

With kind regards

K.

3. ## Re: Independent t-test - interpreting significance if sizes of samples differ greatly

Originally Posted by Karabiner
The software has already corrected the calculations, though (Welch test).
Are you sure about that? In Stata, for example, you actively need to choose to run a Welch test if that is what you want.

4. ## Re: Independent t-test - interpreting significance if sizes of samples differ greatly

This bit:

Reading that line (equality of means not assumed), I see: T=-10.92, 2-tailed sig @ p =.000. Mean diff=-2.01, 95% CI = -2.38 to -1.65.
Suggests that it is a Welch test. In SPSS by default you get both conventional and Welch tests, in a table with rows labelled as equality of variance (not means!) assumed and equality of variances assumed. That's presumably where the OP is getting this from. (In R, Welch is the default, incidentally).

Anyway, Karabiner is right, the unbalanced sample size doesn't change the interpretation of the result

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