Scientific experiments study interventions that show evidence of an effect size that is meaningfully large, negligibly small, or inconclusively broad. Previously, we proposed contra-analysis as a decision-making process to help determine which interventions have a meaningfully large effect by using contra plots to compare effect size across broadly related experiments. Here, we extend the use of contra plots to determine which results have evidence of negligible (near-zero) effect size. Determining if an effect size is negligible is important for eliminating alternative scientific explanations and identifying approximate independence between an intervention and the variable measured. We illustrate that contra plots can score negligible effect size across studies, inform the selection of a threshold for negligible effect based on broadly related results, and determine which results have evidence of negligible effect with a hypothesis test. No other data visualization can carry out all three of these tasks for analyzing negligible effect size. We demonstrate this analysis technique on real data from biomedical research. This new application of contra plots can differentiate statistically insignificant results with high strength (narrow and near-zero interval estimate of effect size) from those with low strength (broad interval estimate of effect size). Such a designation could help resolve the File Drawer problem in science, where statistically insignificant results are underreported because their interpretation is ambiguous and nonstandard. With our proposed procedure, results designated with negligible effect will be considered strong and publishable evidence of near-zero effect size.

翻译：科学实验旨在研究干预措施是否具有显著大、可忽略小或不确定广泛的效应量。此前，我们提出反向分析作为决策流程，通过使用反向图比较广泛相关实验中的效应量，帮助判断哪些干预措施具有显著大的效应。本研究进一步扩展反向图的应用，用于判定哪些结果具有可忽略（接近零）的效应量证据。判断效应量是否可忽略对于排除替代性科学解释、识别干预措施与测量变量之间的近似独立性至关重要。我们证明反向图能够跨研究评估可忽略效应量，基于广泛相关结果指导可忽略阈值的选择，并通过假设检验判定哪些结果具有可忽略效应证据。尚无其他数据可视化方法能同时完成这三项任务。我们以生物医学研究中的真实数据验证了这一分析技术。反向图的新应用可区分统计不显著结果中高证据强度（效应量区间估计窄且接近零）与低证据强度（效应量区间估计宽）的差异。这种区分有助于解决科学中的“抽屉文件”问题——统计不显著结果因解读模糊且非标准化而被低估。通过所提流程，被判定为可忽略效应的结果将被视为接近零效应量的强有力且可发表的证据。