It's really the exceptional case I'm thinking of here. Basically, you screw up such that the data you've collected is meaningless. Sure, you could do the stati

It's really the exceptional case I'm thinking of here. Basically, you screw up such that the data you've collected is meaningless. Sure, you could do the statistical analysis correctly and get a result, but it ends up being the opposite not because the relationship is actually different, but because some part of your design is flawed. In that case, you would never end up with a correct result. (This is of course the worst possible outcome, but I would be remiss if I didn't mention it here.) Although I have no background in it, I suppose the same could happen in the other sciences if there are side effects that you don't anticipate and don't measure accurately enough. ^ This again is why reproducibility is important. Earlier in this thread, @majidazmi mentioned that a major focus of their thesis was to prove that a prior experiment was incorrect in its findings.