One of the pernicious things in this area is that, even as we teach young researchers how to avoid making mistakes and engage sceptically with the work of others and that scientific fraud is a nontrivial issue, we also tell them how to commit fraud themselves and that their competition is doing it.
"Watch out for P-hacking, that's where the researcher uses a form of analysis that has a small chance of a false positive, and analyses loads of subsets of your dataset until a false positive arises and just publishes that one"
"Watch out for over-fitting to benchmarks, like a car taking the speed crown by sacrificing the ability to corner"
"Watch out for incomplete descriptions of test setups, like testing on a 'continent-scale map' but not mentioning how detailed a map it was"
"Watch out for citations where the cited paper doesn't say what is claimed, some people will copy-and-paste citations without reading the source paper"
"Watch out for papers using complicated notation, fancy equations and jargon to make you feel this looks like a 'proper' paper"
"Watch out for deceptive choice of accurate numbers, like a study with a 25% completion rate including the drop-outs in the number of participants"
"Watch out for simulations with inaccurate noise models, if the noise is gaussian in the simulation but a random walk in reality, great simulated results won't transfer to reality"
I've made no suggestion at all that you should modify your science or commit fraud - but I've also just trained you in how to do it.