I am looking to understand the need and intuition for statistical power. I understand how to calculate it, but I cannot understand why we need it. Here is my current thinking:
Suppose we have a control group and an experimental group and we would like to perform a comparison of means. In this case the Null hypothesis is that the difference is 0, while the alternative is that it is non-zero. Assuming that we can invoke the central-limit theorem and that the Null hypothesis is true, we center at normal distribution with mean=0. Then given data, we can calculate the p-value using this normal distribution. Increasing the sample size (number of data points) decreases the variance of this normal distribution and thus the threshold before an effect size becomes significant decreases.
Now assuming I want a minimum effect size of 1%, why can I not choose the sample size in order to decrease the variance of the normal distribution of the Null hypothesis, such that effect sizes under 1% are not significant. Why do I need power analysis to choose the sample size? What am I misunderstanding and what does the Power analysis solve, which I cannot solve using the Null hypothesis and p-values?