In an era of data abundance, statistical evidence is increasingly critical for business and policy decisions. Yet, organizations lack empirical tools to assess the value of evidence-based decision making (EBDM), optimize statistical precision, and balance the costs of evidence-gathering strategies against their benefits. To tackle these challenges, this article introduces an empirical framework to estimate the value of EBDM and evaluate the return on investment in statistical precision and project ideation. The framework leverages parametric and nonparametric empirical Bayes methods to account for parameter heterogeneity and measure how statistical precision changes the value of evidence. The value extracted from statistical evidence depends critically on how organizations translate evidence into policy decisions. Commonly used decision rules based on statistical significance can leave substantial value unrealized and, in some cases, generate negative expected value.

翻译：在数据丰富的时代，统计证据对于商业和政策决策日益关键。然而，组织缺乏实证工具来评估基于证据的决策（EBDM）的价值、优化统计精度，并权衡证据收集策略的成本与收益。为应对这些挑战，本文引入一个实证框架，用于估计EBDM的价值，并评估统计精度与项目构思的投资回报。该框架利用参数化和非参数化经验贝叶斯方法，以考虑参数异质性，并衡量统计精度如何改变证据的价值。从统计证据中提取的价值，关键取决于组织如何将证据转化为政策决策。基于统计显著性的常用决策规则可能导致大量价值未能实现，在某些情况下甚至产生负的期望价值。