This article presents contemporary best-practice recommendations for stated preference (SP) studies used to inform decision-making, grounded in the peer-reviewed literature. These recommendations cover the use of SP methods to estimate both use and non-use values, and include contingent valuation (CV) and discrete choice experiments (CE). The focus is on applications to public goods in environmental and human health contexts, but the recommendations also apply to other areas. The article emphasizes the importance of SP results being used and reused (benefit transfers) by governmental and non-governmental organizations, and highlights the need for clear guidelines to ensure quality and consistency in SP studies. It distinguishes between practices with sufficient research support and those requiring further investigation. The goal is to improve the quality of SP studies and promote research that enhances their global application. SP methods are controversial, with debates over their validity, particularly regarding hypothetical bias. Despite this, they remain essential for estimating non-use values when direct or indirect revealed preference data are unavailable. SP methods have been widely used since the 1980s, with over 7,500 studies published by 2011. They are central to policy analysis, litigation, and decision-making by firms and NGOs. The NOAA Blue Ribbon Panel on CV in 1993 provided initial guidelines, but recent research has shown that these are outdated. The article proposes updated guidelines that reflect current research and address issues such as incentive compatibility, which is crucial for validity. The recommendations are divided into five categories: survey development and implementation, value elicitation, data analysis, validity assessment, and study reporting. They emphasize the importance of clear scenario descriptions, pretesting, and ethical considerations in data collection. The article also discusses the choice between CV and CE methods, highlighting their respective advantages and disadvantages. Experimental design is crucial for efficient and unbiased estimates, and the article provides guidance on designing effective studies. Finally, the article underscores the need for ethical standards in SP research to ensure the protection of human subjects and the reliability of results.This article presents contemporary best-practice recommendations for stated preference (SP) studies used to inform decision-making, grounded in the peer-reviewed literature. These recommendations cover the use of SP methods to estimate both use and non-use values, and include contingent valuation (CV) and discrete choice experiments (CE). The focus is on applications to public goods in environmental and human health contexts, but the recommendations also apply to other areas. The article emphasizes the importance of SP results being used and reused (benefit transfers) by governmental and non-governmental organizations, and highlights the need for clear guidelines to ensure quality and consistency in SP studies. It distinguishes between practices with sufficient research support and those requiring further investigation. The goal is to improve the quality of SP studies and promote research that enhances their global application. SP methods are controversial, with debates over their validity, particularly regarding hypothetical bias. Despite this, they remain essential for estimating non-use values when direct or indirect revealed preference data are unavailable. SP methods have been widely used since the 1980s, with over 7,500 studies published by 2011. They are central to policy analysis, litigation, and decision-making by firms and NGOs. The NOAA Blue Ribbon Panel on CV in 1993 provided initial guidelines, but recent research has shown that these are outdated. The article proposes updated guidelines that reflect current research and address issues such as incentive compatibility, which is crucial for validity. The recommendations are divided into five categories: survey development and implementation, value elicitation, data analysis, validity assessment, and study reporting. They emphasize the importance of clear scenario descriptions, pretesting, and ethical considerations in data collection. The article also discusses the choice between CV and CE methods, highlighting their respective advantages and disadvantages. Experimental design is crucial for efficient and unbiased estimates, and the article provides guidance on designing effective studies. Finally, the article underscores the need for ethical standards in SP research to ensure the protection of human subjects and the reliability of results.