I didn't mention anything about your "common misunderstanding" which I agree with and if you would want to do outlier detection or anything like that you would need to add it separately to your codebase (which is not that difficult to do)
My point was and still is that thinking about Kalman filters just as a measurement filtering system misses the point. An important value of measurements(especially in the more interesting formulations of KF such as the UKF) is updating the covariance matrices over time which affects the priori distributions of the state over time and thus affects your state estimation.
But it still sounds like you're saying the Kalman filter updates the state covariance matrix based on the measurement, which I'm taking to mean the nominal value of a measurement.
My point was and still is that thinking about Kalman filters just as a measurement filtering system misses the point. An important value of measurements(especially in the more interesting formulations of KF such as the UKF) is updating the covariance matrices over time which affects the priori distributions of the state over time and thus affects your state estimation.