Federal agencies in the United States produce a wide range of estimates from increasing sources of data to inform evidence-based policy decisions. Communicating the uncertainty of these estimates and the uncertainty of associated inferences (e.g. trends, comparisons) is essential to transparent quality reporting and making informed decisions. In 2016, the American Statistical Association (ASA) released a statement on the use of significance testing, one tool used for interpreting and communicating the uncertainty of statistical data, recommending a decreased reliance on p-values for decision making. This session brings together a panel to discuss communicating statistical uncertainty for federal agencies, including implications of the 2016 ASA statement, information needs of data users and stakeholders, and some alternatives for communicating statistical uncertainty for evidence-based policy decisions.