An article in Nature takes another (and long-needed) look at this near-religious symbol of scientific correctness:

http://www.nature.com/news/scientific-method-statistical-errors-1.14700

Here is one paragraph to give you a taste:

“One result is an abundance of confusion about what the *P* value means^{4}. Consider Motyl’s study about political extremists. Most scientists would look at his original *P* value of 0.01 and say that there was just a 1% chance of his result being a false alarm. But they would be wrong. The *P* value cannot say this: all it can do is summarize the data assuming a specific null hypothesis. It cannot work backwards and make statements about the underlying reality. That requires another piece of information: the odds that a real effect was there in the first place. To ignore this would be like waking up with a headache and concluding that you have a rare brain tumour — possible, but so unlikely that it requires a lot more evidence to supersede an everyday explanation such as an allergic reaction. The more implausible the hypothesis — telepathy, aliens, homeopathy — the greater the chance that an exciting finding is a false alarm, no matter what the *P* value is.”

and another:

“Many statisticians also advocate replacing the *P* value with methods that take advantage of Bayes’ rule: an eighteenth-century theorem that describes how to think about probability as the plausibility of an outcome, rather than as the potential frequency of that outcome. This entails a certain subjectivity — something that the statistical pioneers were trying to avoid. But the Bayesian framework makes it comparatively easy for observers to incorporate what they know about the world into their conclusions, and to calculate how probabilities change as new evidence arises.”

### Like this:

Like Loading...

*Related*