The requirement that evidence be empirical, which is to say, actual measurements of nature itself, is found too burdensome to new age scientists. They prefer clean, clinical computer models to messy, often uncooperative nature. Over reliance on models, misapplication of statistical methods, and lack of repeatability are the hallmarks of the new pseudoscience that is replacing the traditional practice of science, real science.
The respected journal Nature posted a new article entitled “Online debate erupts to ask: is science broken?” John Ioannidis, a professor at Stanford University’s School of Medicine, recently published a paper with the shocking title “Why Most Published Research Findings Are False.” In “Why Science Is Broken, and How To Fix It,” scientist William M Briggs weighed in on the problem. “There’s been a spate of lamentations that science is broken,” he writes. “I am a credentialed, working scientist, and I’m here to tell you that, with some exceptions, these cris de coeur are right. Science is a mess.”
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.
Bringing all this back to climate science, 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.
Go there and read the whole article. Know why you can’t trust anything coming from the science field today.