Research explores asymptotic normality and variance of chi-square test, highlighting implications for statistical power.
Pearson's chi‐square test is a widely used tool for analyzing categorical data, yet its statistical power has remained theoretically underexplored. Due to the difficulties in obtaining its power function in the usual manner, Cochran (1952) suggested the derivation of its Pitman limiting power, which is later implemented by Mitra (1958) and Meng and Chapman (1966). Nonetheless, this approach is suboptimal for practical power calculations under fixed alternatives. In this work, we solve this long‐standing problem by establishing the asymptotic normality of the chi‐square statistic under fixed alternatives and deriving an explicit formula for its variance. For finite samples, we suggest an improved approximation based on the second‐order Taylor expansion. As a further contribution, we obtain the power functions of two distance covariance tests. We apply our findings to study the statistical power of these tests under different simulation settings.
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Qingyang Zhang (2025) studied this question.
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