In real life, there is always this feedback edge from the results to the methodology.

Theoretically it's unscientific to do tweaks like this but in reality this is what actual science is because you need to see the results understand the problems in your experimental designs.

Now the interesting thing is that you could repair the bias problem. The main issue is that you will do these tweaks after a bad result not after they look fine.

Maybe we could commit ahead of time that the experiment will be reanalyzed after results regardless of what they are. Instead of only when the results disprove the hypothesis.

So like artificial analysis committing to a fixed cadence of index updates instead of when the results start looking jank.