I guess the context is important here. If the first experiment was wrong, and the second experiment is wrong, will you publish the failure of the second experiment? Will you also publish your suspicion that the first experiment was wrong? How likely will people believe you that your results prove the first experiment was wrong, if you did something else?
In practice, individual scientists like to be able to say "my work causes updates". If you do something that rests on someone else's work and the experiment doesn't come out, you have an incentive to say "Someonewrongonthenet's hypothesis X implies A and B. Someonewrongonthenet showed A [citation], but I tried B and that means X isn't completely right.
Cue further investigation which eventually tosses out X. Whether or not A was a false positive is less important than whether or not X is right.
Is there a chance that the process I described was responsible for this?
Yes, that's possible. I'm not sure direct replication actually solves that issue, though - you'd just shift over to favoring false negatives instead false positives. The existing mechanism that works against this is the incentive to overturn other people's work.
Jason Mitchell is [edit: has been] the John L. Loeb Associate Professor of the Social Sciences at Harvard. He has won the National Academy of Science's Troland Award as well as the Association for Psychological Science's Janet Taylor Spence Award for Transformative Early Career Contribution.
Here, he argues against the principle of replicability of experiments in science. Apparently, it's disrespectful, and presumptively wrong.
This is why we can't have social science. Not because the subject is not amenable to the scientific method -- it obviously is. People are conducting controlled experiments and other people are attempting to replicate the results. So far, so good. Rather, the problem is that at least one celebrated authority in the field hates that, and would prefer much, much more deference to authority.