Popular guidance on observational data analysis states that outcomes should be blinded when determining matching criteria or propensity scores. Such a blinding is informally said to maintain the "objectivity" of the analysis, and to prevent analysts from fishing for positive results by exploiting chance imbalances. Contrary to this notion, we show that outcome blinding is not a sufficient safeguard against fishing. Blinded and unblinded analysts can produce bias of the same order of magnitude in cases where the outcomes can be approximately predicted from baseline covariates. We illustrate this vulnerability with a combination of analytical results and simulations. Finally, to show that outcome blinding is not necessary to prevent bias, we outline an alternative sample partitioning procedure for estimating the average treatment effect on the controls, or the average treatment effect on the treated. This procedure uses all of the the outcome data from all partitions in the final analysis step, but does not require the analysis to not be fully prespecified.