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）较低的研究，其出现具有统计学显著性的结果实为假阳性结果（false positive result）的概率会升高。本研究针对探究生物、环境或认知参数与神经、精神及躯体疾病关联的各类研究的荟萃分析（meta-analyses）开展系统综述，且将治疗性研究排除在外，旨在估算上述研究领域的平均统计功效；我们将荟萃分析所报告的效应量（effect size）作为真实效应量的最佳估计值，并设定统计学显著性阈值为5%，结果发现约50%的研究其统计功效处于0~10%或11~20%区间，远低于通常被视为常规标准的80%最低阈值。统计功效较低的研究在生物医学领域似乎颇为常见，至少在本研究检索策略所覆盖的特定主题范畴内是如此；不过，我们也发现证据表明，该现象在一定程度上取决于研究方法学：候选基因研究（candidate gene studies）的平均统计功效极低，而采用认知/行为测量手段的研究则表现出较高的平均统计功效。该结论值得开展进一步探究。