Most Science Studies Tainted by Sloppy Analysis

mlimber writes "The Wall Street Journal has a sobering piece describing the research of medical scholar John Ioannidis, who showed that in many peer-reviewed research papers 'most published research findings are wrong.' The article continues: 'These flawed findings, for the most part, stem not from fraud or formal misconduct, but from more mundane misbehavior: miscalculation, poor study design or self-serving data analysis. [...] To root out mistakes, scientists rely on each other to be vigilant. Even so, findings too rarely are checked by others or independently replicated. Retractions, while more common, are still relatively infrequent. Findings that have been refuted can linger in the scientific literature for years to be cited unwittingly by other researchers, compounding the errors.'"

Comments

How is this news?

[dmsuperman]

We all know that a good portion of all studies are based on bogus facts or on other studies based on other studies based on nothing.

Re:New tactic

[kickedfortrolling]

Im not sure an effect predicted with hundreds of years worth of data can be tested in the 10 years since anthropogenic global warming became fashionable.. I think this may be an excellent example of basic science tainted by obscene analysis.

Something as chaotic as climate must be an easy target for tinkering?

Another global warming point: who is going to turn around and admit they were wrong if they find something contradictory? Denying AGW is basically denying the holocaust these days!

Re:10 years?

[kickedfortrolling]

Hi Ben,

Nice articles, you missed the word 'fashionable' from my post

I dont think the idea that Arrhenius predictions are vaguely similar to the whole IPCC's gives me much confidence that we have progressed in the last 100 years.

Anecdotal longevity also doesnt address the main point which is that we have not tested any of the theories (other than Arrhenius') over anything longer than the average lifetime, and that (as tfa suggests):

Statistically speaking, science suffers from an excess of significance. Overeager researchers often tinker too much with the statistical variables of their analysis to coax any meaningful insight from their data sets.

what faith can we have in people who's entire fields funding depends on their finding new and more horrific predictions?