Bernd:
The original poster wondered about the "real importance" of statistical data rejection of darks and biases. That is why I chimed in with the subtle reason for the use of rejection for bias frames.
To answer the original question, the same logic that is used to determine the rejection type and sigma levels with light frames is basically applied here. Windsorized Sigma Clipping is just fine. Specifying 3-4 sigma for the low and 2-3 for the high is just fine.
Since bias frames (and darks) are noisy- in an ideal situation with no transient events or electronic changes- the mean of the data is desired. So for years I have been quite draconian about the rejection with darks and biases. I used asymmetric min/max clipping. With 50 biases, for example, I would min/max clip (3,5). This means I would be averaging only 42 values of the set. But so what! Biases are cheap! Easy to take and with that kind of clipping- no sigma conditions need to be kept- just find the lowest three and highest 5 values in the set and cut them away!
Concerning electronic changes (interference)- if it is a random (or semi-random, like a phased effect)- rejection will not do anything and it just becomes part of the noise term. If, however, the fluctuations occur in only a few frames in a set... sure rejection will take care of this.