That said, I think the recent spate of "a majority of science publications are wrong" stories is incredible hyperbole. Is it the raw data that is wrong (fabricated)?
This is only a good working assumption of some (open access) journals and of papers (co-)authored exclusively by nationals of some countries. That's a lot of papers.
The main conclusions? One or two minor side points? What if the broad strokes are right but the statistics are sloppy?
If the main conclusions are right but the statistics are sloppy the paper is true, not false.
My confidence in what Ioannidis published went up significantly on learning that epidemiology is mostly bullshit[0] and "Bayer halts nearly two-thirds of its target-validation projects because in-house experimental findings fail to match up with published literature claims, finds a first-of-a-kind analysis on data irreproducibility."[1]
I hope the author of the textbook does not listen to you.
To be accurate the paper should then come to the conclusion that "a majority of published work is inconclusive and fails in the proper application of statistics used to support their claims" instead of "a majority of science publications are wrong". The second is just pure sensationalist troll.
I'm not arguing against the work itself, or against more rigorous application of statistics. I'm just arguing against sensationalistic and inflammatory language. Anyone who practices science in a particular field for any length of time will have a pretty good idea of what work is "good" and "bad". Certainly they are smart enough to ignore previous work that has been refuted and/or retracted, and it's not really fair for this previous work to contribute to assessments of what % of the field is "wrong".
This is only a good working assumption of some (open access) journals and of papers (co-)authored exclusively by nationals of some countries. That's a lot of papers.
The main conclusions? One or two minor side points? What if the broad strokes are right but the statistics are sloppy?
If the main conclusions are right but the statistics are sloppy the paper is true, not false.
My confidence in what Ioannidis published went up significantly on learning that epidemiology is mostly bullshit[0] and "Bayer halts nearly two-thirds of its target-validation projects because in-house experimental findings fail to match up with published literature claims, finds a first-of-a-kind analysis on data irreproducibility."[1]
I hope the author of the textbook does not listen to you.
[0]http://lesswrong.com/lw/72f/why_epidemiology_will_not_correc...
[1]http://blogs.nature.com/news/2011/09/reliability_of_new_drug...