Statistical hypothesis testing seems to come into and out of vogue in different fields at different times. In the past decade, many ecologists have vehemently criticized the use of hypothesis
tests and P-values in data analysis. 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. Using a two-sample comparison as an example, I show the obvious connections of P-values to confidence intervals and to Akaike's information criterion
(AIC), two metrics that have been advocated as replacements for the P-value. The choice of a threshold value of delta-AIC that breaks ties among competing models is as arbitrary as the choice
of the probability of a Type I error in significance testing, and several other criticisms of the P-value apply equally to delta-AIC.

Since P-values, confidence intervals, and delta-AIC are based on the same statistical information, all have their places in modern statistical practice. The choice of which to use should be
dictated by details of the application at hand, rather than by dogmatic, a priori considerations.