You likely have heard of the replication crisis going on, where past research findings cannot be reproduced by other researchers using the same methods. The issue, typically, lies with p-value, an essential but limited statistic that we use to establish statistical significance. (There are other replication problems than just p-value, but that’s the one that you read about the most.) You can read about p-value here and the replication crisis here.
These problems are often associated with the social sciences in general and the fields of psychology and education specifically. This is largely due to the inherent complexities of human-subject research, which typically involves many variables that researchers cannot control; the inability to perform true control-grouped experimental studies due to practical or ethical limitations; and the relatively high alpha thresholds associated with these fields, typically .05, which are necessary because effects studied in the social sciences are often weak compared to those in the natural or applied sciences.
However, it is important to be clear that the p-value problem exists in all manner of fields, including in some that are among the “hardest” of scientific disciplines. In a 2016 story for Slate, Daniel Engber writes of much cancer research, “much of cancer research in the lab—maybe even most of it—simply can’t be trusted. The data are corrupt. The findings are unstable. The science doesn’t work,” because of p-value and associated problems. In a 2016 article for the Proceedings for the National Academy of Sciences of the United States, Eklund, Nichols, and Knutsson found that inferences drawn from fMRI brain imaging are frequently invalid, sharing concerns voiced in a 2016 eNeuro article by Katherine S. Button about replication problems across the biomedical sciences. A 2016 paper by Erik Turkhemier, an expert in genetic heritability of behavioral traits, discussed the ways that even replicable weak associations between genes and behavior prevent researchers from drawing meaningful conclusions about the relationship between genes and behavior. In a 2014 article for Science, Erik Stokstad expressed concerns that ecology literature was more and more likely to list p-values, but that the actual explained effects were becoming weaker and weaker, and that p-values were not adequately contextualized through reference to other statistics.
Clearly, we can’t reassure ourselves that p-value problems are found only in the “soft” sciences. There is a far broader problem with basic approaches to statistical inference that affect a large number of fields. The implications of this are complex; as I have said and will say again, research nihilism is not the answer. But neither is laughing it off as a problem inherent just to those “soft” sciences. More here.