Disclaimer: I'm not sure if there's a better stackexchange for this, but since I consider experimental design an integral part of causal/statistical inference, I'm hoping this is an appropriate audience.
So, I'm considering setting up a controlled experiment on a service that has users (each have unique
user_ids). To determine which users get put into which group ("control" or "treatment"), an engineer has proposed the following assignment algorithm:
if (md5(user_id) mod 100 < control_fraction) assign user_id to control else assign user_id to treatment
But I'm still left with lingering doubts as to whether or not this assignment mechanism may introduce some sort of subtle selection/enrollment biases...
Does this scheme allow me to proceed with my experimental analysis, as if we had assigned users randomly, or do I need to take anything into account?