Halley (2003) proposed that parameter drift decreases the uncertainty in long-range extinction risk estimates, because drift mitigates the extreme sensitivity of estimated risk to estimated mean growth rate. However, parameter drift has a second, opposing effect: it increases the uncertainty in parameter estimates from a given data set. When both effects are taken into account, parameter drift can increase, sometimes substantially, the uncertainty in risk estimates. The net effect depends sensitively on the type of drift and on which model parameters must be estimated from observational data on the population at risk. In general, unless many parameters are estimated from independent data, parameter drift increases the uncertainty in extinction risk. These findings suggest that more mechanistic PVA models, using long-term data on key environmental variables and experiments to quantify their demographic impacts, offer the best prospects for escaping the high data requirements when extinction risk is estimated from observational data.