A standard way to elicit expectations asks for the percentage chance an event will occur. Previous research demonstrates noise in reported percentages. The current research models a bias; a five percentage point change in reported probabilities implies a larger change in beliefs at certain points in the probability distribution. One contribution of my model is that it can parse bias in beliefs from biases in reports. I reconsider age and gender differences in Subjective Survival Probabilities (SSPs). These are generally interpreted as differences in survival beliefs, e.g., that males are more optimistic than females and older respondents are more optimistic than younger respondents. These demographic differences (in the English Longitudinal Study of Ageing) can be entirely explained by reporting bias. Older respondents are no more optimistic than younger respondents and males are no more optimistic than females. Similarly, in forecasting, information is obscured by taking reported percentages at face value. Accounting for reporting bias thus better exploits the private information contained in reports. Relative to a face-value specification, a specification that does this delivers improved forecasts of mortality events, raising the pseudo R-squared from less than 3 percent to over 6 percent.