Earlier quoted context omitted.
As a scientist I think you are addressing a very important problem with this book. I've taken two statistics classes, one graduate level, and even I am plagued with doubt as to wether the statistics I've used have all been applied and interpreted "correctly". 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 (fabrica…
If, say, you find that your results are not statistically sigificant at a reasonable p-level, you don't get to claim your conclusion is right in 'broad strokes', maybe has some 'significant errors', a bit 'sloppy statistics', but it's broadly true, right? Nope, instead it's not statistically significant, so there's no reason to think it wasn't just random chance that made it 'close' to statistically significant. But…
Or what if one of the experiments in a paper is well-designed and well-executed and supports a hypothesis with very high certainty but some of the other experiments were sloppy or botched? Should the conclusions of the entire paper be labeled as "wrong"?
I find it pretty funny when critiques on statistical rigor in science arrive at language with words such as "mostly" and "wrong".