Measuring tiny airborne particles by their movement in an electric field is common in atmospheric science, but the results almost never include uncertainty estimates. The largest source of uncertainty in these measurements relates to particle charging. We developed a Bayesian method to systematically quantify this uncertainty. Applied to a month of atmospheric data, accounting for charging uncertainty widened the plausible range of particle concentrations by a factor of two to ten.