Small follow-up question: I'm curious how your sample size calculator chooses its value. Traditional power analysis asks for two proportions rather than for a p

Small follow-up question: I'm curious how your sample size calculator chooses its value. Traditional power analysis asks for two proportions rather than for a proportion and an effect size. So the sample size for a positive effect differs from that for a negative effect (from the same baseline). I'd imagine your tool would conservatively present the larger of the two. However, it seems to present something near the midpoint of the two. Is that the intention? Or is there some other statistics being used here? (For example, with an 8% baseline conversion rate, a 1% absolute detectable different, 85% power and 10% significance level, your tool says 10,583 per branch. R's `power.prop.test` gives sample sizes of 11,182 for a positive change (8% vs 9%) and 9,974 for a negative change (8% vs 7%). The exact midpoint is 10,578.)