  How many experimental studies would have come to different conclusions had
they been run on larger samples? I show how to estimate the expected number of
statistically significant results that a set of experiments would have reported
had their sample sizes all been counterfactually increased by a chosen factor.
The estimator is consistent and asymptotically normal. Unlike existing methods,
my approach requires no assumptions about the distribution of true effects of
the interventions being studied other than continuity. This method includes an
adjustment for publication bias in the reported t-scores. An application to
randomized controlled trials (RCTs) published in top economics journals finds
that doubling every experiment's sample size would only increase the power of
two-sided t-tests by 7.2 percentage points on average. This effect is small and
is comparable to the effect for systematic replication projects in laboratory
psychology where previous studies enabled accurate power calculations ex ante.
These effects are both smaller than for non-RCTs. This comparison suggests that
RCTs are on average relatively insensitive to sample size increases. The policy
implication is that grant givers should generally fund more experiments rather
than fewer, larger ones.
