Depends. Throwing outliers out without thinking is obviously wrong. In many instances outliers can be just invalid measurements and you should ignore them.
> In many instances outliers can be just invalid measurements and you should ignore them.
signal[i] = value[i] + noise[i].
If you know that value[i] == NaN, then by all means throw out signal[i]. If value[i] != NaN, then you're better off modeling error[i], and using that model to give you information about value[i] as yummyfajitas suggests.
This is trivial to see if noise[i] == 0, but for some reason becomes progressively harder for people as noise[i] increases.
