Empowering peer reviewers with a checklist to improve transparency

Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.

More than 500 controlled experiments have collectively suggested that biodiversity loss reduces ecosystem productivity and stability. Yet the importance of biodiversity in sustaining the world’s ecosystems remains controversial, largely because of the lack of validation in nature, where strong abiotic forcing and complex interactions are assumed to swamp biodiversity effects. Here we test this assumption by analysing 133 estimates reported in 67 field studies that statistically separated the effects of biodiversity on biomass production from those of abiotic forcing. Contrary to the prevailing opinion of the previous two decades that biodiversity would have rare or weak effects in nature, we show that biomass production increases with species richness in a wide range of wild taxa and ecosystems. In fact, after controlling for environmental covariates, increases in biomass with biodiversity are stronger in nature than has previously been documented in experiments and comparable to or stronger than the effects of other well-known drivers of productivity, including climate and nutrient availability. These results are consistent with the collective experimental evidence that species richness increases community biomass production, and suggest that the role of biodiversity in maintaining productive ecosystems should figure prominently in global change science and policy.