A survey of colleagues and recent periodicals was used to characterize how biologists use statistics, with an emphasis on applications in ecology and evolutionary biology. Biologists use statistics to describe and uncover patterns in data and to ensure that they are obtaining reliable knowledge. Universally, sampling and study design problems plague biological research programs, and this continues to be one of the greatest needs for statistical training. Biologists traditionally have used a Neyman-Pearson approach to hypothesis testing, combined with an overbearing focus on the Fisherian notion of statistical significance and P-values. Multivariate statistics became popular 20 years ago but were used less following the publication of a few critical reviews that questioned the validity of statistical inferences. Advances in nonlinear dynamics that grew from computer technology have stimulated new methods for time-series analysis capable of capturing underlying structures in temporal data. Similarly, computer technology supporting geographical information systems (GIS) has led to widespread application and development of spatial statistics. Recently, Bayesian statistics have found support in a few applications, but it is my impression that most biologists are either suspicious of Bayesian methods, do not understand them, or have a difficult time implementing them. Computer-intensivesimulation methods are commonly used for inference in volving complex models. Biologists are recognizing that statistics have applications beyond hypothesis testing, and we are seeing increased use of likelihood approaches, such as the Akaike information criteria, for model selection. Repeatedly, we find that technological advances in computing power have revolutionized how biologists use statistics.