On Asymptotic Standard Normality of the Two Sample Pivot

TL;DR

This paper demonstrates that the asymptotic normality of the two-sample pivot for comparing means holds under broader conditions than previously thought, without requiring independence or specific sample size ratios.

Contribution

It shows that the convergence to a standard Normal distribution occurs if the joint distribution of standardized means is spherically symmetric, relaxing previous assumptions.

Findings

Asymptotic normality does not require sample independence.

The result holds without restrictions on sample size ratios.

Spherical symmetry of the joint distribution implies normality.

Abstract

The asymptotic solution to the problem of comparing the means of two heteroscedastic populations, based on two random samples from the populations, hinges on the pivot underpinning the construction of the confidence interval and the test statistic being asymptotically standard Normal, which is known to happen if the two samples are independent and the ratio of the sample sizes converges to a finite positive number. This restriction on the asymptotic behavior of the ratio of the sample sizes carries the risk of rendering the asymptotic justification of the finite sample approximation invalid. It turns out that neither the restriction on the asymptotic behavior of the ratio of the sample sizes nor the assumption of cross sample independence is necessary for the pivotal convergence in question to take place. If the joint distribution of the standardized sample means converges to a spherically symmetric distribution, then that distribution must be bivariate standard Normal (which can happen without the assumption of cross sample independence), and the aforesaid pivotal convergence holds.