Estimates of variance in pharmacological assays are usually made by repeating the experiment with different tissues. Biological factors, such as the inability to wash a drug from tissue, may preclude the type of replication that is appropriate for the statistics of interest. For example, in Schild regressions, replication is usually done at each concentration of antagonist. In some test systems, replication of dose-response curves is not possible. For example, some persistent agonists cannot be removed from tissues after exposure, while in other systems, rapid desensitization severely alters tissue sensitivity to repeated challenge with agonist. In this paper, we demonstrate how a statistical resampling method, bootstrapping, can be used to derive estimates of the confidence intervals for pA2, pKB, and slope from Schild plots. This method utilizes the speed of the computer to estimate variance by repeatedly resampling the data. The advantage to this method is that it can be used for many different experimental designs. For a data set obtained from a Schild regression of atenolol antagonism of isoproterenol in the guinea pig left atrium, bootstrap estimates of confidence limits were calculated for cases where dose ratios were derived from the same tissue and randomly paired tissues. These estimates showed good agreement with estimates obtained using conventional analytical methods, thus suggesting that this method may be useful in practice.