I think part of the point there is not to pass effect estimates through a significance test filter first. Most studies are underpowered to detect a true effect at alpha = 0.05. That doesn't actually suggest that most studies are wrong as much as if a study is underpowered and doesn't find a significant finding, we assert its dull and uninteresting.
Ironically, the Ioannidis paper is in Epidemiology, which is a journal that is fairly anti-significance testing, but where I still get reviewers suggesting that an effect measure with a confidence interval that brushes against the null must mean nothing at all.
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I think part of the point there is not to pass effect estimates through a significance test filter first. Most studies are underpowered to detect a true effect at alpha = 0.05. That doesn't actually suggest that most studies are wrong as much as if a study is underpowered and doesn't find a significant finding, we assert its dull and uninteresting.
Ironically, the Ioannidis paper is in Epidemiology, which is a journal that is fairly anti-significance testing, but where I still get reviewers suggesting that an effect measure with a confidence interval that brushes against the null must mean nothing at all.