Statisticians and data scientists find insights that help lead to better understanding and better outcomes. When clients and managers come to us for help (and even when they don't), we want to share our advice. While we should be free to share our recommendations, we need to be clear about what the data is telling us and what is based "only on our judgment". Gelman, et. al. wrote "As we have learned from the replication crisis sweeping the biomedical and social sciences, it is frighteningly easy for motivated researchers working in isolation to arrive at favored conclusions-whether inadvertently or intentionally." One senior business leader I know said, "if you have data, great; if we're just going on intuition we can use mine". However, having data isn't enough. We need to be rigorous in our analysis to avoid finding insights that aren't supported. This paper will go through a number of examples to illustrate common mistakes.

翻译：统计学家和数据科学家通过发现洞见，助力更深入的理解与更优的成果。当客户和管理者向我们寻求帮助时（甚至在他们未主动寻求时），我们倾向于分享专业建议。虽然我们有权提出建议，但必须明确区分数据本身揭示的信息，以及那些"仅基于个人判断"的结论。Gelman等人曾指出："从席卷生物医学与社会科学领域的可重复性危机中我们认识到，受动机驱动的科研人员在孤立状态下开展工作——无论有意还是无意——都极易得出偏好的结论。"我相识的一位高级商业领袖曾表示："若有数据支撑固然好；若仅凭直觉判断，我倒不如沿用己见。"然而，仅拥有数据并不足够。我们必须以严谨态度进行分析，避免发现缺乏实证支持的所谓"洞见"。本文将通过多个案例，系统阐释常见的数据分析谬误。