I'm pretty sure this was discussed in an earlier HN post. However, I want to point out that "not replicating" doesn't necessarily equate to bad science (or, more accurately, bad practice on the part of the principal investigators irt accepted best practices in their field). There are a bunch of studies in the social sciences that failed to replicate simply because a p value was slightly below a threshold target, even…
Failure to replicate is the definition of bad science. Science is based on objective, repeatable experiments. If the experiments can't even be reproduced how can they be hypothesized and validated?
Also, I don’t think you understand that there is a difference between “replicate” and “reproduce” as those terms are typically used in social and biological science. Reproduction refers to the methods and is simply conducting the experiment again, regardless of the outcome. An experiment should be reproducible, of course—if it isn’t, the paper was poorly written (the experiment might still be ok though).
Replication refers to the results: do we get the same answers as the original experiment? Bad science is one way an experiment might not replicate but other reasons are 1. Just a regular type 1 error in the original study, 2. A type 2 error in the new one, 3. an overlooked methodological difference or mistake somewhere. No. 3 doesn’t mean bad science, it could be just normal scientists making normal mistakes, as they sometimes do.
Also, you don’t “hypothesize” an experiment. You come up with a hypothesis then test it with an experiment. (Sometimes scientists HARK but that’s a different discussion …)
In this case, it sounds like there truly was some cheating going on of the worst kind (data fabrication).