So there was this famous experiment and just recently a new team of researchers tried to replicate it and they could not. Quoting Alex Tabarrok:
You will probably not be surprised to learn that the new paper fails to replicate the priming effect. As we know from Why Most Published Research Findings are False (also here), failure to replicate is common, especially when sample sizes are small.
There’s a lot more at the MR link you should check it out. But here’s the thing. If most published research findings are false then which one is the false one, the original or the failed replication? Have you noticed that whenever a failed replication is reported, it is reported with all of the faith and fanfare that the original, now apparently disproven study was afforded? All we know is that one of them is wrong, can we really be sure which?
If I have to decide which to believe in, my money’s on the original. Think publication bias and ask yourself which is likely to be larger: the number of unpublished experiments that confirmed the original result or the number of unpublished results that didn’t.
Here’s a model. Experimenters are conducting a hidden search for results and they publish as soon as they have a good one. For the original experimenter a good result means a positive result. They try experiment A and it fails so they conclude that A is a dead end, shelve it and turn to something new, experiment B. They continue until they hit on a positive result, experiment X and publish it.
Given the infinity of possible original experiments they could try, it is very likely that when they come to experiment X they were the first team to ever try it. By contrast, Team-Non-Replicate searches among experiments that have already been published, especially the most famous ones. And for them a good result is a failure to replicate. That’s what’s going to get headlines.
Since X is a famous experiment it’s not going to take long before they try that. They will do a pilot experiment and see if they can fail to replicate it. If they fail to fail to replicate it, they are going to shelve it and go on to the next famous experiment. But then some other Team-Non-Replicate, who has no way of knowing this is a dead-end, is going to try experiment X, etc. This is going to continue until someone succeeds in failing to replicate.
When that’s all over let’s count the number of times X failed: 1. The number of times X was confirmed equals 1 plus the number of non-non-replications before the final successful failure.