Article: Odds Are, Its Wrong

Science fails to face the shortcomings of statistics. This article is not a condemnation of science, math, or statistics.   It does point out problems, particularly with the use of statistics to develop assumptions.  Much of the article focuses on medical studies.   The author also highlights solutions.

Some interesting excerpts  from the article

“Science was seduced by statistics, the math rooted in the same principles that guarantee profits for Las Vegas casinos. Supposedly, the proper use of statistics makes relying on scientific results a safe bet. But in practice, widespread misuse of statistical methods makes science more like a crapshoot.”

“Even when performed correctly, statistical tests are widely misunderstood and frequently misinterpreted.”

“In fact, if you believe what you read in the scientific literature, you shouldn’t believe what you read in the scientific literature.”

“There are more false claims made in the medical literature than anybody appreciates,” he (epidemiologist John Ioannidis)  says. “There’s no question about that.”

““Replication is vital,” says statistician Juliet Shaffer, a lecturer emeritus at the University of California, Berkeley. And in medicine, she says, the need for replication is widely recognized. “But in the social sciences and behavioral sciences, replication is not common,” she noted in San Diego in February at the annual meeting of the American Association for the Advancement of Science. “This is a sad situation.””

[Bayesian Math]

“Bayesian math seems baffling at first, even to many scientists, but it basically just reflects the need to include previous knowledge when drawing conclusions from new observations…

With the increasing availability of computer power to perform its complex calculations, the Bayesian approach has become more widely applied in medicine and other fields in recent years. In many real-life contexts, Bayesian methods do produce the best answers to important questions. In medical diagnoses, for instance, the likelihood that a test for a disease is correct depends on the prevalence of the disease in the population, a factor that Bayesian math would take into account.”