The P value
Posted: Tue Apr 03, 2018 7:20 am
A proposal to lower the P value threshold for significance from 0.05 to 0.005:
https://jamanetwork.com/journals/jama/f ... le/2676503
I can't wait to see how many studies that have been used to influence medical care would be instantly negated. I've long been dubious about the idea that every living human should undergo regular diagnostic testing and take multiple preventive medications without an individualized benefit/risk/cost assessment. It seems I'm not such a nut ball after all.
My emphasis. If only Machine Ghost were still around!
https://jamanetwork.com/journals/jama/f ... le/2676503
I can't wait to see how many studies that have been used to influence medical care would be instantly negated. I've long been dubious about the idea that every living human should undergo regular diagnostic testing and take multiple preventive medications without an individualized benefit/risk/cost assessment. It seems I'm not such a nut ball after all.
Multiple misinterpretations of P values exist, but the most common one is that they represent the “probability that the studied hypothesis is true.” A P value of .02 (2%) is wrongly considered to mean that the null hypothesis (eg, the drug is as effective as placebo) is 2% likely to be true and the alternative (eg, the drug is more effective than placebo) is 98% likely to be correct. Overtrust ensues when it is forgotten that “proper inference requires full reporting and transparency.” Better-looking (smaller) P values alone do not guarantee full reporting and transparency. In fact, smaller P values may hint to selective reporting and nontransparency. The most common misuse of the P value is to make “scientific conclusions and business or policy decisions” based on “whether a P value passes a specific threshold” even though “a P value, or statistical significance, does not measure the size of an effect or the importance of a result,” and “by itself, a P value does not provide a good measure of evidence.”
These 3 major problems mean that passing a statistical significance threshold (traditionally P = .05) is wrongly equated with a finding or an outcome (eg, an association or a treatment effect) being true, valid, and worth acting on. These misconceptions affect researchers, journals, readers, and users of research articles, and even media and the public who consume scientific information. Most claims supported with P values slightly below .05 are probably false (ie, the claimed associations and treatment effects do not exist). Even among those claims that are true, few are worth acting on in medicine and health care.
My emphasis. If only Machine Ghost were still around!