Unless the data collection itself is biased. The solution to that is to have another team collect its own data and compare its findings. But now you once again have scientists doing science, and if those scientists confirm the other findings, then they might also have used biased methods, so you once again have to either trust them, or not. So to build trust, yet more scientists have to collect and analyze yet more data. But what if
those scientists are also in on the bias? So maybe at this point, you hire your own scientists that you trust implicitly because you're the one paying them. Maybe their findings contradict all the earlier findings, and so you trust that. But now nobody
else trusts their findings, because you paid them for the study, so people think they are probably biased toward your perspective. This is literally what has been going on for years.
So now what? How do you collect and analyze data when trust in those doing the collection and analysis is not a given?
One approach is to implicitly trust scientists, because they are experts in their field, while you are a novice. This is what a lot of us do, and I think it's a perfectly fine solution, but it is a trade-off; you might misplace your trust.
Another approach is to train up on the science yourself, so that you can independently verify things. This is a great solution, but it isn't scalable. Most people have other jobs and other interests, and in any case, you can never be an expert in everything that matters.
Another approach might be to try to build trust using out-of-band signals, like transparent communication. Transparency often sounds nice, but it also has a major chilling effect, because people communicate more efficiently in the sorts of candid conversations that are hard to have publicly.
Personally, I think the witch hunt for biased climate scientists is disgusting and beside the point, but I also find it distasteful to suggest that it isn't a really hard problem to disseminate trusted information about complex subjects in a highly specialized society.