Statistical hypothesis testing has been widely criticized by ecologists in recent years. I review some of the more persistent criticisms of P values and argue that most stem from misunderstandings or incorrect interpretations, rather than from intrinsic shortcomings of the P value. I show that P values are intimately linked to confidence intervals and to differences in Akaike's information criterion (ΔAIC), two metrics that have been advocated as replacements for the P value. The choice of a threshold value of ΔAIC that breaks ties among competing models is as arbitrary as the choice of the probability of a Type I error in hypothesis testing, and several other criticisms of the P value apply equally to ΔAIC. Since P values, confidence intervals, and ΔAIC are based on the same statistical information, all have their places in modern statistical practice. The choice of which to use should be stylistic, dictated by details of the application rather than by dogmatic, a priori considerations.