  The popularity of online surveys has increased the prominence of using
weights that capture units' probabilities of inclusion for claims of
representativeness. Yet, much uncertainty remains regarding how these weights
should be employed in the analysis of survey experiments: Should they be used
or ignored? If they are used, which estimators are preferred? We offer
practical advice, rooted in the Neyman-Rubin model, for researchers producing
and working with survey experimental data. We examine simple, efficient
estimators for analyzing these data, and give formulae for their biases and
variances. We provide simulations that examine these estimators as well as real
examples from experiments administered online through YouGov. We find that for
examining the existence of population treatment effects using high-quality,
broadly representative samples recruited by top online survey firms, sample
quantities, which do not rely on weights, are often sufficient. We found that
Sample Average Treatment Effect (SATE) estimates did not appear to differ
substantially from their weighted counterparts, and they avoided the
substantial loss of statistical power that accompanies weighting. When precise
estimates of Population Average Treatment Effects (PATE) are essential, we
analytically show post-stratifying on survey weights and/or covariates highly
correlated with the outcome to be a conservative choice. While we show these
substantial gains in simulations, we find limited evidence of them in practice.
