Sperm head abnormalities are associated with excessive omega‐6 fatty acids in two finch species feeding on sunflower seeds

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.

Increasing evidence from animal and in vitro studies indicates that n-3 fatty acids, especially the long-chain polyunsaturated fatty acids eicosapentaenoic acid and docosahexaenoic acid, present in fatty fish and fish oils inhibit carcinogenesis. The epidemiologic data on the association between fish consumption, as a surrogate marker for n-3 fatty acid intake, and cancer risk are, however, somewhat less consistent. This review highlights current knowledge of the potential mechanisms of the anticarcinogenic actions of n-3 fatty acids. Moreover, a possible explanation of why some epidemiologic studies failed to find an association between n-3 fatty acid intake and cancer risk is provided. Several molecular mechanisms whereby n-3 fatty acids may modify the carcinogenic process have been proposed. These include suppression of arachidonic acid-derived eicosanoid biosynthesis; influences on transcription factor activity, gene expression, and signal transduction pathways; alteration of estrogen metabolism; increased or decreased production of free radicals and reactive oxygen species; and mechanisms involving insulin sensitivity and membrane fluidity. Further studies are needed to evaluate and verify these mechanisms in humans to gain more understanding of the effects of n-3 fatty acid intake on cancer risk.