A reminder not to be overly impressed when presented with statistically significant coefficients, from FiveThirtyEight.com.
The Economist reports about research by Paul Smaldino and Richard McElreath indicating that studies in psychology, neuroscience and medicine have low statistical power (the probability to correctly reject a null hypothesis). If, nevertheless, almost all published studies contain significant results (i.e., rejections of null hypotheses), then this is suspicious.
Furthermore, Smaldino and McElreath’s research suggests that
the process of replication, by which published results are tested anew, is incapable of correcting the situation no matter how rigorously it is pursued.
With the help of a model of competing research institutes, Smaldino and McElreath simulate how empirical scientific research progresses. Labs that find more new results also tend to produce more false positives. More careful labs try to rule out false positives but publish less. More “successful” labs are allowed to replicate. As a consequence, less careful labs spread out. Replication—repetition of randomly selected findings—does not stop this process.
poor methods still won—albeit more slowly. This was true in even the most punitive version of the model, in which labs received a penalty 100 times the value of the original “pay-off” for a result that failed to replicate, and replication rates were high (half of all results were subject to replication efforts).
Smaldino and McElreath conclude that “top-performing laboratories will always be those who are able to cut corners”—even in a world with frequent replication. The Economist concludes that
[u]ltimately, therefore, the way to end the proliferation of bad science is not to nag people to behave better, or even to encourage replication, but for universities and funding agencies to stop rewarding researchers who publish copiously over those who publish fewer, but perhaps higher-quality papers.