In an open letter, L. Rafael Reif, President of MIT, encourages Americans to oppose the White House’s budget plans.
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.
In the Journal of Economic Perspectives, Tyler Cowen and Alex Tabarrok question whether NSF funds are allocated efficiently. They write:
First, a key question is not whether NSF funding is justified relative to laissez-faire, but rather, what is the marginal value of NSF funding given already existing government and nongovernment support for economic research? Second, we consider whether NSF funding might more productively be shifted in various directions that remain within the legal and traditional purview of the NSF. Such alternative focuses might include data availability, prizes rather than grants, broader dissemination of economic insights, and more. …
Public goods theory tells us that the National Science Foundation should support activities that are especially hard to support through traditional university, philanthropic, and private-sector sources. This insight suggests a simple test: to the extent that the NSF allocates funds to genuine public goods as opposed to subsidies on the margin, we ought to see a large difference in the kinds of projects the NSF supports compared to what the “market” sector supports. But what stands out from lists of prominent NSF grants … is how similar they look to lists of “good” research produced by today’s status quo.