A common misinterpretation of the statistician’s P value is that it measures how likely it is that a null (or “no effect”) hypothesis is correct. Actually, the P value gives the probability of observing a result if the null hypothesis is true, and there is no real effect of a treatment or difference between groups being tested. A P value of .05, for instance, means that there is only a 5 percent chance of getting the observed results if the null hypothesis is correct.

It is incorrect, however, to transpose that finding into a 95 percent probability that the null hypothesis is false. “The P value is calculated under the assumption that the null hypothesis is true,” writes biostatistician Steven Goodman. “It therefore cannot simultaneously be a probability that the null hypothesis is false.”

Consider this simplified example. Suppose a certain dog is known to bark constantly when hungry. But when well-fed, the dog barks less than 5 percent of the time. So if you assume for the null hypothesis that the dog is not hungry, the probability of observing the dog barking (given that hypothesis) is less than 5 percent. If you then actually do observe the dog barking, what is the likelihood that the null hypothesis is incorrect and the dog is in fact hungry?

Answer: That probability cannot be computed with the information given. The dog barks 100 percent of the time when hungry, and less than 5 percent of the time when not hungry. To compute the likelihood of hunger, you need to know how often the dog is fed, information not provided by the mere observation of barking.

## Thursday, March 18, 2010

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