Table S2. Characteristics of included studies from Low statistical power in biomedical science: a review of three human research domains

Studies with low statistical power increase the likelihood that a statistically significant finding represents a false positive result. We conducted a review of meta-analyses of studies investigating the association of biological, environmental or cognitive parameters with neurological, psychiatric and somatic diseases, excluding treatment studies, in order to estimate the average statistical power across these domains. Taking the effect size indicated by a meta-analysis as the best estimate of the likely true effect size, and assuming a threshold for declaring statistical significance of 5%, we found that approximately 50% of studies have statistical power in the 0–10% or 11–20% range, well below the minimum of 80% that is often considered conventional. Studies with low statistical power appear to be common in the biomedical sciences, at least in the specific subject areas captured by our search strategy. However, we also observe evidence that this depends in part on research methodology, with candidate gene studies showing very low average power and studies using cognitive/behavioural measures showing high average power. This warrants further investigation.

统计功效（statistical power）偏低的研究，其统计学显著性结果实为假阳性结果的概率会相应升高。本研究针对探究生物学、环境或认知参数与神经、精神及躯体疾病关联的各类研究所开展的荟萃分析（meta-analysis）进行了系统综述，并排除了治疗类研究，旨在估算上述研究领域的平均统计功效。以每项荟萃分析所报告的效应量（effect size）作为对应真实效应量的最优估计值，并设定5%作为统计学显著性判定阈值，我们发现约50%的研究统计功效处于0%~10%或11%~20%区间，远低于学界普遍认可的80%最低标准。统计功效偏低的研究在生物医学领域中似乎颇为常见，至少在本研究检索策略覆盖的特定学科范围内是如此。不过，我们也发现该现象在一定程度上取决于研究方法：候选基因研究的平均统计功效普遍极低，而采用认知/行为测量手段的研究则平均统计功效较高。这一问题值得开展进一步研究。