Statistical significance functions as an arbiter of sorts for data analysis purporting to show a relationship between two or more variables. Unfortunately, in far too many situations, statistical significance may lead decision-makers relying on data and analytics to improve business decisions astray, particularly in the context of big data. In this article, I outline reasons why executives should develop a healthy discernment when they see the phrase “statistically significant” in media outlets, internal analyses, consulting reports, and other sources. To overcome the limitations of focusing on statistical significance, I propose executives shift their attention toward the effect size reported from a statistical model. While not without limitation, effect sizes are more useful to decision-makers, highlight the practical implication of analyses, and help in quantifying the uncertainty inherent to working with data.

统计显着性作为数据分析的仲裁者，旨在显示两个或多个变量之间的关系。不幸的是，在太多情况下，统计意义可能会导致决策者依赖数据和分析来改进业务决策，尤其是在大数据的背景下。在本文中，我概述了为什么高管在媒体、内部分析、咨询报告和其他来源中看到“具有统计意义”这个词时应该培养健康的洞察力。为了克服关注统计显着性的局限性，我建议高管将注意力转移到统计模型报告的效应大小上。虽然并非没有限制，但效应量对决策者更有用，强调分析的实际意义，