So actually, I think the idea that data-dependent hypotheses are bad is fundamentally wrong, and is based on a misunderstanding of probability. The reason you'

So actually, I think the idea that data-dependent hypotheses are bad is fundamentally wrong, and is based on a misunderstanding of probability. The reason you'd avoid data-dependent hypotheses is simple: if the data comes from a process with some sort of randomness in it, then there will usually be things that appear to be interesting patterns but are in fact random artifacts. If you look at your data, you may be tempted to formulate a hypothesis based on these random artifacts. It may pass statistical tests (because the data you have does contain the pattern), but it is not, in fact, causal. To avoid this, you maintain the discipline of only making hypotheses before you look at your data, which means that you can't see a random effect and then guess that it's real. The problem is, this doesn't mean that if your hypothesis passes a statistical test, the result must be causal. It only lowers the probability - there is still a chance that your hypothesis was wrong, but your data happens to contain a random fluctuation that makes it look right. The only way to protect against this danger is to continuously gather data and re-evaluate your hypotheses, while understanding that there is always some probability that the effect you think you see is really random noise. And once you're doing this continuous monitoring anyway, then there's no reason to reject data-dependent hypotheses. By definition, if the effect you're seeing is a random occurrence, then it should go away with more data. If it doesn't go away, then maybe you've found something that you wouldn't have been able to guess in advance, which is good! And if you see a random effect, form a hypothesis that passes some test, and then assume that your hypothesis must be true, then the problem is not your data, but rather that you misunderstand how probability works. In short, avoiding data-dependent hypotheses is a hack that only reduces the probability of an error that you should be avoiding entirely anyway. Once you accept this and start avoiding the error, there's no reason to avoid data-dependent hypotheses, and they can be quite useful.