What causes the divorce between reality and representation? Biases

An excellent piece on the mechanism by which cognitive pollution is generated and passes into the public square where it can take years to clean up. From How to Create Scientific Myths Without Really Trying by Lee Jussim.

How does scientific psychology create completely false myths?

Wait. This can't be. I must be wildly overstating something. False? Myths? Its science!

There were experiments! They had "statistically significant findings!" There are a zillion published studies; they can't all be wrong. Can they?

It's the wrong set of questions and objections. It is not that all the studies are "wrong." It's that the conclusions can become unhinged from the evidence.

It is possible because of selectivity in reporting and describing results. You can think of it as "data-laundering," an empirical cousin of "idea laundering," which refers to the ways in which some academic fields have created entire intellectual eco-systems (journals, conferences, courses) that package their political values and goals as "scholarship."

Data-laundering is the related process by which data high in uncertainty is transformed to justify clear and compelling conclusions. How is that possible? De Vries et al. showed how:

Click to enlarge.

De Vries et al. (2018) found over 100 trials examining the effectiveness of antidepressant interventions and basically found that the published, peer-reviewed scientific literature was little better than pure myth. What is going on here?

The first column on the left shows the raw studies. About half the trials were negative (red, meaning either no effect or an effect in the wrong direction, e.g., the intervention did more harm than good). About half were positive (green, the intervention seemed to alleviate depression).

The second column from the left shows which got published (publication bias). You can see visually that even though the studies were split almost 50-50 regarding what they found, about two-thirds of the studies that got published had positive results, and only about one-third of those getting negative results got published.

The third column shows "outcome reporting bias." That refers to selectivity, or slant, with respect to what got reported.

So, to use a hypothetical example that I made up just to explain this point, let's say some study had three outcomes: One showed that the intervention made things worse, one showed no effect, and one showed it made things better. Overall, there was no effect (negative study). But if the researchers only report the positive result, through the magic of selective reporting, the study can appear to have produced a positive result. Most studies are now "positive."

The fourth column from the left refers to spin. Even if the paper honestly reported mixed or weak outcomes, the abstract or discussion characterized the findings as effective. Nearly all studies are now "positive."

The final column is citation bias. Even after all that? There is still more bias. Studies "finding" (or at least spun as finding) positive effects are cited nearly three times as much as studies finding negative effects!

By the end? We have a "scientific" literature literally filled with celebrations of the effectiveness of these anti-depression interventions built on an actual scientific literature composed of half failures.

And that, gentle reader, is how scientific myths are created using "normal" scientific processes. Do these problems go well beyond anti-depression research? For now, no one knows for sure, but:

Now those are some dangerous biases.

Good stuff and the rest of the article is worth reading. Basically, there is an Overton Window of sanctioned beliefs which are promulgated independent of actual reality.