When we perform two tailed test (z-test for proportion, ttest or even bootstrap) the null hypothesis is that there is no difference or samples come from same distribution and alternative hypothesis is that there is a difference or that samples come from different distributions.
Technically in case of two tailed test p-value is the probability of getting difference between two means / proportions greater or equal to the present difference from my experiment having that null hypothesis is true. And this difference could go both sides, it is an absolute difference. So basically two tailed test doesn't say which mean / proportion is greater. It doesn't show direction. It just says that there is a difference.
I know that there is a one tailed test. But it doesn't make sense to me: when I use one tailed test I get significant results twice as often. And I also know that actually it is almost never appropriate to use one tailed test. And in everyday life say with A/B testing we always use two tailed tests. I've personally never seen one tailed test in business practice.
My question is how it is possible to make decisions after two sided test and for example in case of A/B test to say that mean / proportion in one group is greater than other? Test doesn't show the direction of the difference, but we say that one greater than other and make our decision. Am I missing something?)