Propensity‐score matching is frequently used to estimate the effect of treatments, exposures, and interventions when using observational data. An important issue when using propensity‐score matching is how to estimate the standard error of the estimated treatment effect. Accurate variance estimation permits construction of confidence intervals that have the advertised coverage rates and tests of statistical significance that have the correct type I error rates. There is disagreement in the literature as to how standard errors should be estimated. The bootstrap is a commonly used resampling method that permits estimation of the sampling variability of estimated parameters. Bootstrap methods are rarely used in conjunction with propensity‐score matching. We propose two different bootstrap methods for use when using propensity‐score matching without replacement and examined their performance with a series of Monte Carlo simulations. The first method involved drawing bootstrap samples from the matched pairs in the propensity‐score‐matched sample. The second method involved drawing bootstrap samples from the original sample and estimating the propensity score separately in each bootstrap sample and creating a matched sample within each of these bootstrap samples. The former approach was found to result in estimates of the standard error that were closer to the empirical standard deviation of the sampling distribution of estimated effects.