When analyzing decision-level data from more than one economic experiment, the pooled ordinary least squares (OLS) estimator is a weighted sum of (i) within-experiment treatment effects, and (ii) an estimate of between-experiment treatment effects. The latter is plausibly biased and receives substantial weight in typical studies. I discuss some implications of this weighting and some remedies to the problem.