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Scaling a numeraire e-variable by an independent uniform multiplier guarantees a head-to-head win against any rival test with at least 50% probability, yet fundamentally degrades sequential power and expected log-growth.
Upper-bounding the number of distribution shifts in sequential data is provably impossible without parametric assumptions, but conformal inference can uniquely deliver valid, non-trivial lower bounds.