Null hypothesis significance testing and effect sizes: can we 'effect' everything … or … anything?

The Null Hypothesis Significance Testing (NHST) paradigm is increasingly criticized. Estimation approaches such as point estimates and confidence intervals, while having limitations, provide better descriptions of results than P-values and statements about significance levels. Their use is supported by many statisticians. The effect size approach is an important part of power and sample size calculations at the experimental design stage and in meta-analysis and in the interpretation of the biological importance of study results. Care is needed, however, to ensure that such effect sizes are relevant for the endpoint. Effect sizes should not be used to interpret results without accompanying limits, such as confidence intervals. New methods, especially Bayesian approaches, are being developed; however, no single method provides a simple answer. Rather there is a need to improve researchers understanding of the complex issues underlying experimental design, statistical analysis and interpretation of results.