Abstract
The p-value has long been the figurehead of statistical analysis in biology, but its position is under threat. p is now widely recognized as providing quite limited information about our data, and as being easily misinterpreted. Many biologists are aware of p's frailties, but less clear about how they might change the way they analyse their data in response. This article highlights and summarizes four broad statistical approaches that augment or replace the p-value, and that are relatively straightforward to apply. First, you can augment your p-value with information about how confident you are in it, how likely it is that you will get a similar p-value in a replicate study, or the probability that a statistically significant finding is in fact a false positive. Second, you can enhance the information provided by frequentist statistics with a focus on effect sizes and a quantified confidence that those effect sizes are accurate. Third, you can augment or substitute p-values with the Bayes factor to inform on the relative levels of evidence for the null and alternative hypotheses; this approach is particularly appropriate for studies where you wish to keep collecting data until clear evidence for or against your hypothesis has accrued. Finally, specifically where you are using multiple variables to predict an outcome through model building, Akaike information criteria can take the place of the p-value, providing quantified information on what model is best. Hopefully, this quick-and-easy guide to some simple yet powerful statistical options will support biologists in adopting new approaches where they feel that the p-value alone is not doing their data justice.