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This is a concept that I have always struggled to understand: We can write the formula for a Two Sampled T- Test (https://en.wikipedia.org/wiki/Student%27s_t-test) to compare the sample averages from two populations as follows:

$$ t = \frac{\bar{x}_1 - \bar{x}_2}{\sqrt{\frac{(n_1-1)s_1^2 + (n_2-1)s_2^2}{n_1 + n_2 - 2}}\sqrt{\frac{1}{n_1}+\frac{1}{n_2}}} $$

In university, we are always told that for the results of this test to be valid, the distribution of both samples collected MUST be Normally Distributed - yet we are never told why exactly this is. After much thinking and consideration, I think that if the both the sizes of both samples are large, this Normally Distributed condition is not required.

Below is my logic to demonstrate why this is not the case for a specific example where $n_1 = n_2$ and $s_1 = s_2$.

Part 1: First, take the numerator of above term and divide by $ \frac{\sigma}{\sqrt{n}} $. Using the Central Limit Theorem, this modified numerator is now (asymptotically) Normally Distributed regardless of the distributions of $x_1$ and $x_2$:

$$ \frac{\bar{x}_1 - \bar{x}_2}{\sigma/\sqrt{n}} \sim \mathcal{N}(0,1) $$

Part 2: Since we divided the numerator by $ \frac{\sigma}{\sqrt{n}} $ , we also have to divide the denominator by $ \frac{\sigma}{\sqrt{n}} $.

Using some algebra, we can write the denominator as:

$$ \sqrt{\frac{(n-1)s^2 + (n-1)s^2}{2n - 2}}\sqrt{\frac{1}{n}+\frac{1}{n}} = \sqrt{2} \cdot \left(\frac{s}{\sigma}\right) $$

Now we proceed to divide the above term by $ \frac{\sigma}{\sqrt{n}} $ :

$$ \frac{\sqrt{2} \cdot \left(\frac{s}{\sigma}\right)}{\sigma/\sqrt{n}} = \frac{s}{\sigma} $$

Now, using Moment Generating Functions, we can show that the following is asymptotically true (https://online.stat.psu.edu/stat414/lesson/26/26.3) regardless of any underlying distribution:

$$(n-1)\frac{S^2}{\sigma^2} \sim \chi^2_{n-1}$$

Part 3: And finally, when we divide the results from Part 1 and Part 2 (A normal divided by the $\sqrt{\chi^2(s)/s}$ gives you a t-distribution -- proof), this results into a T-Distribution:

$$ \frac{\text{N}(0,1)}{\sqrt{\chi^2_{n-1}/(n-1)}} \sim \text{T-distribution}_{n-1} $$

Therefore, we have shown that regardless of the distribution for any two samples, provided the sizes of both samples are large enough - the T-Test DOES NOT require both of these samples to be Normally Distributed.

For simplicity sake, I did this for $n_1 = n_2$ and $s_1 = s_2$, but I think that the results will also hold for when this is not the case , i.e. $n_1 \neq n_2$ and $s_1 \neq s_2$.

Can someone please comment on my mathematical analysis? Have I done this correctly?

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  • $\begingroup$ I'm not going to validate your 'home work'. We are now in the 21st century and I wish that the two-sample t-test (and ANOVA) be taught as an ordinary least squares linear model which then leads to generalised least squares, generalised linear models etc. that can accommodate non-normal data, non-constant variance, serial correlation, autocorrelation and so on. Moreover, testing of assumptions is more appropriately done with the model residuals and not on the raw data. Similarly, I wish that correlation analyses be replaced by regression. $\endgroup$
    – stweb
    Jul 9, 2023 at 3:52
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    $\begingroup$ Many posts already on site discuss these issues at length. Please consider trying the search function. $\endgroup$
    – Glen_b
    Jul 9, 2023 at 5:19

1 Answer 1

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When the two samples come from populations with normal distributions, there is a theoretical guarantee the test statistic will have the claimed $t$ distribution with the claimed degrees of freedom. When you lack that normality, you no longer get that the test statistic follows such a distribution. In that sense, yes, normality is required for such a test.

However, the test is rather robust to deviations from this normality when sample sizes get large, due to the combination of the central limit theorem and Slutsky's theorem, which I have heard jointly referred to as the converging-together lemma. In that sense, you can still have the test perform close to perfect when the samples are rather non-normal. Your analysis is alluding to this (though I think it misses the part about Slutsky's theorem).

It is possible, perhaps even likely, to be in a situation where the solid robustness is not enough to overcome the gross non-normality. Glen_b has a simulation where this happens for a lognormal distribution. It also makes sense when you consider that the robustness comes from convergence theorems that only say what happens in the limit, not in a finite sample.

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