Limiting distributions of the non-central $t$-statistic and their applications to the power of $t$-tests under non-normality

Abstract

Let $X_1,X_2$,… be a sequence of independent and identically distributed random variables. Let $X$ be an independent copy of $X_1$. Define $\mathbb{T}_{n}=\sqrt{n}\bar{X}/S$, where $\bar{X}$ and $S^2$ are the sample mean and the sample variance, respectively. We refer to $\mathbb{T}_{n}$ as the central or non-central (Student’s) $t$-statistic, depending on whether $\mathrm{E}X=0$ or $\mathrm{E}X≠0$, respectively. The non-central $t$-statistic arises naturally in the calculation of powers for $t$-tests. The central $t$-statistic has been well studied, while there is a very limited literature on the non-central $t$-statistic. In this paper, we attempt to narrow this gap by studying the limiting behaviour of the non-central $t$-statistic, which turns out to be quite complicated. For instance, it is well known that, under finite second-moment conditions, the limiting distributions for the central $t$-statistic are normal while those for the non-central $t$-statistic can be non-normal and can critically depend on whether or not $\mathrm{E}X=∞$. As an application, we study the effect of non-normality on the performance of the $t$-test.