Two multisite trials can have the same informativeness yet differ in how reliably individual site effects can be estimated. We show that the informativeness index averages site-specific reliabilities on the logit scale and is a Bregman centroid under Bernoulli-Kullback-Leibler divergence. This explains the geometric mean of sampling variances and yields a measure of profile dispersion. Under stated conditions, we relate profile dispersion to estimation error and show when balanced allocation maximizes informativeness at fixed total enrollment. Simulations show that estimator performance depends on the reliability profile and whether the target is individual effects, ranks, or their distribution. An online credit recovery trial illustrates the diagnostics and their conditional posterior uncertainty. We recommend reporting informativeness with arithmetic mean reliability and profile dispersion. The accompanying R package poolinfo provides tools for computing the diagnostics, posterior summaries, and design calculations.

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