Our model is useful for understanding how (i) measurement noise and (ii) variance in the concentration of the interferent (a) confound the relationship between the response of the sensor and the concentration of the analyte and thereby (b) corrupt the predicted (from the response of the sensor) concentration of the analyte. In the deployment of the catalytic filter-gas sensor, the objective is to solve an inverse problem: predict the concentration of the analyte given the response of the sensor. Prediction error emanates from both (i) measurement noise and (ii) the (necessary to lend a well-posed inverse problem) assumption that the concentration of the interferent is fixed at its mean. We impose probability distributions of (a) concentrations of the analyte and interferent in the sensing environment and (b) measurement noise, then compute the expected error in the predicted concentration of the analyte-a performance metric of the catalytic filter-gas sensor. Supporting our hypothesis, our toy model captures the Goldilocks problem in the performance of the sensing device as a function of residence time of the gas in the catalytic filter.