This gets into deep philosophical questions. Logic can be deductive or inductive. Your use of formal logic implies deductive logic, but empirical science is ind

This gets into deep philosophical questions. Logic can be deductive or inductive. Your use of formal logic implies deductive logic, but empirical science is inductive. With induction, one can only have negative knowledge (this is know as the problem of induction or the black swan problem), but it gets more complicated than that. Most studies are trying to look at probabilistic outcomes and are trying to get significant p-values. There is a myriad of problems with this beyond simply the existing probability that their sample was bias. E.g., their methodology could be improper, or even just less than ideal. There could even be unknown problems with the underlying assumptions and the model being used by researches. To suggest that an opposite result refutes a previously obtained result deeply mis-characterizes how science and human understanding works, and the problems inherent in this thought process deeply overvalues the knowledge we have and undervalues the more-than-likely seriously uncertain data we work with every day. If you're interested in these problems, i'd suggest Karl Popper's work on philosophy of science.