The two totals are independent, with and . Their joint likelihood is
Differentiating the separated log-likelihood gives
These formulas include all-zero or all-one totals as boundary maxima. For interior , the Fisher information in the ordered parameter vector is
The off-diagonal entries vanish because the likelihood separates, and the two estimates are independent even at finite sample size. For large , their joint distribution is approximately
A linear combination of independent normal variables is normal; the corresponding asymptotic statement follows from the joint central limit theorem and the continuous mapping theorem. Thus
The two variances add because of independence. This approximation assumes both sample sizes grow and the true probabilities are interior.
Under the null, replace both parameters by a common . The likelihood becomes
The pooled maximum-likelihood estimate is
Under an interior null , part (ii) gives a centered asymptotic normal difference with variance . The law of large numbers makes in probability. By Slutsky's theorem, replacing by this consistent estimate gives
For the two-sided alternative, reject when , the corresponding standard-normal quantile. This has asymptotic significance level .
For a two-sided 5% test, the critical values are . Since , do not reject the null at the 5% level.
For the prespecified directional alternative , the numerator tends to be negative. Use the lower-tail critical region at the 5% level. Since , reject in favor of in the one-sided test. The direction should be chosen from the additional scientific information rather than selected after inspecting the sign of this same statistic.

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