Science death spiral

He then takes solid aim at the overuse, indeed the misuse of statistics as a crutch for sloppy research.

The second problem is ritual. Many, many fields rely on probability models for their livelihood (the bulk of sociology, for example). But almost none of the people using these models gets them right. Everybody knows the old saw “correlation is not causation”, but just as the guy in the pew doesn’t think the sermon is meant for him, most scientists don’t think they’ve fallen prey to the fallacy of confusing correlation and cause, though many of them (particularly in the soft sciences) have.

I myself have seen the rampant spread of statistics at all levels of science. In the past I have served as a judge at the state science fair, held at a local university where I have taught on occasion. An astounding number of young researchers held up a cooperative p-value as unassailable proof that their experimental results were spot on. When I commented on this to one of the other judges, a biologist, I received an almost indignant reply that the p-value was the “statistical gold standard” and that they were ingraining this in all their students. The problem here is not doing the calculations—there are many packages that will do that—it is in the construction of the experiment, the validity of the assumptions that frame the null hypotheses, and the interpretation of the results.

Clear scientific thinking has been supplanted by a magical statistical blessing. Stating “The hunt for p-values less than 0.05 has left many of science’s roadways riddled with potholes,” Hilda Bastian offers up “Tips For Avoiding P-Value Potholes,” though whether others will heed her warnings remains to be seen. So wide spread is the abuse of the p-value that the editors of Basic and Applied Social Psychology (BASP) announced that the journal would no longer publish papers containing p-values because the statistics were too often used to support lower-quality research.

Yet another article proclaims “the replication crisis in science has just begun.” Its summary states: “After a decade of slow growth beneath public view, the replication crisis in science begins breaking into public view. First psychology and biomedical studies, now spreading to many other fields — overturning what we were told is settled science, the foundations of our personal behavior and public policy.”

The author focuses on the core problem of reproducibility, because results that cannot be reproduced are useless in science. Posted on the Fabius Maximus blog, it is a treasure trove of links to other article regarding science's malaise. Included is the central cite for monitoring retraction of scientific research, the appropriately title Retraction Watch.

Bringing all this back to climate science (you knew I would) in a stunning new paper, "Climate Modeling Dominates Climate Science," by Patrick J. Michaels and David E. Wojick, the extent of over reliance on climate modeling in climate science has been exposed. The research paper surveyed the entire literature of science for the last ten years, using Google Scholar, looking for modeling. They found that climate change science accounts for fully 55% of the modeling done in all of science. Quoting the article:

In fact the number of climate change articles that include one of the three modeling terms is 97% of those that just include climate change. This is further evidence that modeling completely dominates climate change research.

This shows how fake climate science "research" really is while at the same time tarnishing the reputation of computer modeling, which is a useful tool when applied properly. It's not just GCM, every aspect of climate science has been infected with modeling fever (see “Of Models And Melting Ice Caps”). What's more, modeling is spreading to other fields of inquiry, tempting researchers to invent their own computer realities rather than investigate nasty, inconvenient nature.

To summarize, the following are the factors that are eroding the pillars of science.

• Unreproducible results – through shoddy work, poor experiment design, and statistical ignorance more and more results reported in papers can not be reproduced, making them scientifically useless.


• Corruption by politics – whether through group think or government funding the pressure to conform to politically acceptable results has increased to the point working scientists either submit to consensus or stay quiet.


• Statistical malpractice – through lack of training or sloth, many scientists use statistics as a drunk uses a lamp post, for support, not for illumination.


• Reliance on computer modeling – computer modeling is a wonderful tool when looking for insight but they are not faithful representations of nature itself. When scientists end up studying their models instead of nature they are no longer scientists.


• Misuse of peer review – instead of functioning as academic quality control and an aid to authors, peer review has become the enforcer of consensus thinking and scientific dogma. Instead of helping science advance it ensures conformity.