Let be the ordered eigenpairs of the common covariance operator , and write . Estimate by the pooled covariance operator
and denote its first eigenpairs by . The sign ambiguity of each eigenfunction disappears after squaring. Consider the Two-sample FPCA mean statistic
Under , the Hilbert-space central limit theorem gives
where is a centered Gaussian random element with covariance . The assumed eigenvalue gaps give consistency of the estimated eigenvalues and eigenfunctions, so Slutsky's theorem yields
An asymptotic level- test therefore rejects when exceeds the -quantile of the chi-squared distribution with degrees of freedom.
Under a fixed alternative,
The test is consequently consistent whenever the mean difference has a nonzero projection onto one of the retained principal components. A difference orthogonal to their span is invisible to this fixed- test, so alone does not guarantee consistency. Increasing with can recover such alternatives, but requires additional eigenvalue and approximation control.

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