Do cryptocurrency markets process infrastructure failures differently from regulatory shocks? We study both moments of the return distribution on one shared sample (50 events, six assets, 2019-2025), fitting a GJR-GARCH-X model under matched dependence-robust inference. We treat event inclusion as a measured design parameter: rather than asserting the selection-on-the-dependent-variable objection away, we trace the variance differential across the inclusion screen and measure the selection bias directly. The result is a scope condition -- under curated, high-salience identification the differential is sizeable ($4.88\times$) but selection-conditional: a mechanical impact filter on a broad reconstructed pool collapses it to $1.3$-$1.6\times$. Identification is half the story; inference is the other. The curated multiplier is not distinguishable from zero once cross-asset dependence and heavy tails are respected: a Student-$t$-copula CCC-GARCH-X bootstrap (our inference of record) returns $p \approx 0.32$, and because the six per-asset coefficients are strongly cross-correlated the contrast's effective sample size is nearer three than six (design-effect $p \approx 0.07$-$0.15$). A naive i.i.d. test had reported an apparently decisive fivefold effect, but that significance was an artefact: pseudoreplication across correlated assets compounded by a heavy-tail-misspecified bootstrap. The first moment tells the same story -- a $+7.19$ pp cumulative-abnormal-return difference a block bootstrap cannot distinguish from zero ($p = 0.283$). Under correct inference the asymmetry is directional but unresolved. The contribution is a portable inference toolkit -- an inference ladder and a Monte-Carlo size study -- for diagnosing how cross-asset event studies in heavy-tailed markets manufacture significance, demonstrated where it dissolves a fivefold result the author had himself published.

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