I know that the power of an experiment/study is determined by significance value $\alpha$, the effect size, and the sample size.
The power of an experiment increases if either of those increase.
And both an underpowered as well as an overpowered experiment are bad.
I understand the problem with underpowered experiments: if $\alpha$ is too low we may not find anything, if the effect size is too small then $H_0$ and $H_1$ are so close they may be the same distribution. And to small a sample size, that's obviously bad they may not represent their population good enough.
I've encountered 3 reasons why an overpowered experiment is bad: it takes up too many samples which is expensive or unethical and "tiny effects may create a significant result e.g. the fact that we measured on a Monday". I only care about the last one.
I understand how a too large $\alpha$ would be bad, by definition we would have a higher probability of falsely finding an effect.
Assuming the effect size to be higher than it is would mean to overestimate the significance of our findings, that makes sense. But a larger actual effect size shouldn't be a problem, should it?
Now for the sample size I have no explanation. If anything, I would expect more measurements to represent the population better! I would expect more measurements to even out small biases!
What am I getting wrong here?