> Statistics has probably done more damage to the world than any other discipline, by giving a sheen of respectability to fake science in fields like nutrition, psychology, economics, and medicine.
This seems really unfair. You can misuse statistics, but it's an extremely powerful tool when it's properly used and understood. Most powerful tools can be misused - you can write terrible code and (try to) publish bad mathematics too. But much of modern science would be intractable without statistics; including physics, chemistry, biology and applied math, because we'd be otherwise unable to draw reasonable conclusions from anything less than a totality of data.
As someone with a graduate education in probability snd statistics, I think it's fair to lay some of the blame for the reproducibility crisis at the feet of statisticians because of poor education. Statisticians should accept at least some responsibility if their students in non-math majors graduate without understanding the material, for sure.
But that being said, it should definitely be noted that actual statisticians have been talking about this crisis for decades. Statisticians have basically always known that there's nothing magical about the 95% significance threshold p <= 0.05, for example. And for the most part, it's not statisticians who are causing the bad science to occur. Rather it's a problem of non-statisticians using statistics without (qualified) peer review that they can't be expected to do correctly if it's not their core competency.
In my opinion it's something of a philosophical problem - many fields and journals are only realizing now that it's unreasonable to expect a e.g. professional psychologist to also be an expert statistician. Having a dedicated statistician - instead of another psychologist who hasn't reviewed the material since their upper undergrad course - is a giant leap forward in catching bad stats in new research.
This seems really unfair. You can misuse statistics, but it's an extremely powerful tool when it's properly used and understood. Most powerful tools can be misused - you can write terrible code and (try to) publish bad mathematics too. But much of modern science would be intractable without statistics; including physics, chemistry, biology and applied math, because we'd be otherwise unable to draw reasonable conclusions from anything less than a totality of data.
As someone with a graduate education in probability snd statistics, I think it's fair to lay some of the blame for the reproducibility crisis at the feet of statisticians because of poor education. Statisticians should accept at least some responsibility if their students in non-math majors graduate without understanding the material, for sure.
But that being said, it should definitely be noted that actual statisticians have been talking about this crisis for decades. Statisticians have basically always known that there's nothing magical about the 95% significance threshold p <= 0.05, for example. And for the most part, it's not statisticians who are causing the bad science to occur. Rather it's a problem of non-statisticians using statistics without (qualified) peer review that they can't be expected to do correctly if it's not their core competency.
In my opinion it's something of a philosophical problem - many fields and journals are only realizing now that it's unreasonable to expect a e.g. professional psychologist to also be an expert statistician. Having a dedicated statistician - instead of another psychologist who hasn't reviewed the material since their upper undergrad course - is a giant leap forward in catching bad stats in new research.