Required sample size to distinguish means of two binomially distributed rv


Currently trying to work out the best way to calculate the number of samples that would be required to get a certain confidence interval. If I have two binomially distributed data sets, which I predict to have some probability of an event p_1 and p_2.

If I were to estimate p_1 and p_2 by measuring "n" sample points from each set I would obtain some estimates p_1(n) and p_2(n). This gives me a difference [p_1(n) - p_2(n)]. If I want to show with some confidence interval "sigma" that for some delta_p I have

delta_p > |p_1(n) - p_2(n)|

what expression would I have "n" such that these conditions are satisfied in terms of p_1, p_2, delta_p and sigma?

Either that or can someone point me in the direction of some helpful literature? I assume it is some sort of inverse "t" distribution technique.

Many thanks