Short observational pricing panels often contain many observations but few distinct price movements. We evaluate the inferential consequences of this sparsity in a synthetic data-generating process by separating estimation error into uncertainty conditional on a realized price trajectory and variation across alternative trajectories. In baseline simulations, this across-design component accounts for 97.6% of estimation error variance for a gradient-boosted specification, causing coverage shortfalls driven by design-specific centering error that standard within-panel resampling and cluster-robust procedures fail to capture. Three main results organize the analysis. First, across-design dispersion follows the empirical relation sigma_b approx 0.182 V^(-0.271), where V = n_moves * magnitude^2, with -0.271 treated as a simulation regularity. Second, adding regions sharing a common price path improves nuisance estimation but creates no independent price trajectories; only averaging across units with independent design errors reduces across-design standard deviation at the sqrt(k) rate. Third, a Paule-Mandel variance component estimated across independently priced units increases empirical coverage under homogeneity from 0.469 to 0.931. Broadly, improving inference in passive panels requires generating independent identifying variation, such as through controlled regional randomization. Finally, an application to scanner data (Dominick's Finer Foods, Soft Drinks) confirms these findings: nominal price zones and products behave as a small fraction of their count in independent design draws, yielding between-unit dispersion intervals far wider than conventional within-panel bootstraps.

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