Real example: when you're working on the data analysis of one of the "beyond the standard model" physics experiments. For example, there is one where they basically shoot a big laser against a wall and see if anything goes through. Spoiler: it won't.

Such an experiment will usually see nothing and claim an upper bound on the size of some hypothetical effect (thus essentially ruling it out). Such a publication would be reviewed and scrutinized rather haphazardly. Regardless, the results are highly publishable and the scientists working on it are well respected.

Alternatively, the experiment might see something and produce a publication that would shatter modern understanding of physics, which means it would be strongly reviewed and scrutinized and reproduction attempts would happen.

Since the a-priori probability of such an experiment finding something is absurdly low, the second case would almost always lead to an error being found and the scientists involved being shamed. Therefore, when you do data analysis for such an experiment, especially if you want your career to move on to a different field or to industry, you always quickly find ways to explain and filter away any observation as noise.

And no, a lot of them don't use data blinding...