To be clear, I'm not against funding replication studies. That's a great thing that should be done. The risk is that if it were done people would think - great! There was a problem but science is fixed now. That wouldn't be true. The clear majority of papers I've read that were bad/unusable in some way in the past 10 years wouldn't have been helped by funding replication studies.

> (1) (3) (4) you seem to be making the assumption that replication studies are basically done by robots blindly following an instruction manual of someone else's research.

I guess we're using the word replication differently. You seem to be using it to mean a general re-review and re-analysis of everything - basically a funded more aggressive peer review followed by an actual re-running of the study sometimes, if it makes sense, whereas I'm using it to just mean re-running the study exactly as originally described to check the results are the same.

I think in science the term replication normally just means re-running the study exactly as described. It doesn't mean arguing with the authors over their definitions or the logical basis of the study itself. I've seen a bunch of cases where a replication fails and the original study team basically rejects the whole exercise by saying "They didn't follow our instructions so of course they got different results". And I mean, that's kind of fair, right? If you say "I did X and saw Y", and then someone else says it's not true but they didn't actually do X, surely the scientists have every right to be annoyed and reject the exercise? So good replications are exactly what they sound like - a replication of the original process. They aren't generalized adversarial funded peer reviews.

And that's why it's important to highlight that replication is only one aspect. If Congress or whomever goes and earmarks money for replication, then someone with money will eventually ask you to replicate a study with a flawed design. What do you do? You could:

a. Say no: the study is flawed. Replication would be useless because you'd just be repeating a nonsensical procedure. You get no money.

b. Take the money and re-run it. The study conclusions are still wrong but now you got a paper published, and the original is successfully replicated so journalists/professors will use that as a stamp of approval. You may even want the study to replicate because it's professionally or ideologically useful.

c. Try to 'fix' the original design and do an improved study. This just starts the process over from scratch, it doesn't yield evidence.

It'll be (b) or (c) of course, every time. That's just how the incentives are set up and besides, doing another study can be done without involving the original authors. The moment you go down route (a) you're going to get pushback. They won't agree their definitions are illogical. So, it'll devolve into an exchange of letters that nobody ever sees or cares about, and granting agencies won't know how to value it.

> 2. Effect sizes varying in replicated studies is still useful information. Not all "does/doesn't replicate" questions need a binary yes/no answer.

But the bureaucracy needs actionable outcomes, otherwise there's no point. If someone can point at a replication and say "ah yes, we claimed our educational intervention would boost grades by 2x in 15 year olds and governments spent money on that basis, but a replicated 0.1% improvement nonetheless proves us right" then nobody outside the system will consider this a valid replication. There must be actual outcomes from a failure to replicate meaning you'd have to draw the line at how much delta is allowable from the original numbers, but nobody is even having that discussion let alone doing anything about it.

For (5), it was more of a question than anything else. Replication using originally collected data might be useful sometimes but the question is whether the general public would consider this to be a genuine cross-check. My guess is no. Science has no formal mechanisms to detect made up or fraudulent data sets, which is a real problem, so if you allow re-analysis of originally collected data you'll get a steady stream of situations where a study is announced, some skeptics say "uhhh that sounds wrong", the media/academic institutions beat up on them claiming it's peer reviewed and replicated which is gold standard so you've got to believe, and then it turns out the whole original study + replications were all based on fraud. What could be more damaging than that? A replication is a type of audit. These have to take into account the possibility of people playing games for profit.