[throwawaymaths]

Even if it's a few. Imagine if honest researchers start chasing the fraudulent results. Now you have several people's time wasted. If the honest researcher is junior (PhD or Postdoc), their career is almost certainly over. Worse, assume the junior researcher is dishonest or marginal. The incentive is to fudge things a little bit to keep a career. The cycle begins anew... inherent in our system there is positive selection (in the 'natural selection' sense) for dishonest researchers.

This should give you pause.

Without claiming that any given administration is taking any action with deliberateness or planning... What is even more counterintuitive is that if the dishonesty hits a certain critical point, defunding all research suddenly is net positive.

I would also suggest you keep your ear to the ground. Almost every scientific discipline is in a crisis of reproducibility right now.

[sokka_h2otribe]

You might think crisis of reproducibility means everyone is faking data. No, that does not mean that. There are many factors to a crisis of reproducibility. One is fake data. A bigger one is a lack of incentive and a lack of complete data gathering details on some metric. Generally even if there is a crisis is subjective.

There's also usually a mismatch between what older scientists and younger scientists think are the right approach to studying something.

But generally, science is pretty good. You're reading small slices and assuming it actually represents all of science. It doesn't. Please give me a better sense of what ground your ear is on. I don't think it's generally representative of most science fields. Science has a cool thing where you could post totally fake data, but there are enough actors that also would question it if it's entirely unreproducible. Most issues are small nudges or selective data (e.g, retesting when data doesn't support your expectations), not blatant lies. The blatant lie stories you hear are not actually common and I'd love to hear where you think they are.

[throwawaymaths]

> Most issues are small nudges or selective data (e.g, retesting when data doesn't support your expectations), not blatant lies.

Yeah you missed it. When you do small nudges or selectively report data that's even worse than faking data. Not all villains twirl their mustaches. It's the ones that don't that are the most dangerous, these are the ones that are going to suck time and effort away from the collective endeavour the worst. Everyone knows that leclair can't do synthesis. But how certain are we that Phil Baran's Xenon oxidation really worked?