The article by Gary King, Michael Tomz, and Jason Wittenberg from Harvard University addresses the common issue in social science research where statistical results are not fully utilized, leading to missed opportunities to present meaningful quantities and express appropriate uncertainty. They propose a method based on statistical simulation to extract and present overlooked information in a reader-friendly manner. The authors argue that this approach can enrich the substance of social science research, improve the candor and realism of statistical discourse, and make results accessible to a broader audience. The article outlines the problem of statistical interpretation, emphasizing the need to go beyond simple interpretations and statistical significance to convey substantive quantities. It introduces the concept of simulation as a tool to compute quantities of interest and their uncertainties, providing algorithms for predicting values, expected values, and first differences. The authors also discuss alternative methods such as Bayesian techniques, bootstrapping, and analytical approaches like the delta method, highlighting their advantages and limitations. To illustrate their approach, the authors replicate and enhance the interpretations of several published works, showing how their methods can reveal new insights and improve the clarity and precision of statistical results. They also provide an easy-to-use software package called "CLARIFY" to implement their suggestions, which has won awards for its effectiveness and ease of use. Overall, the article aims to guide social scientists in making more informative and transparent use of statistical data, ensuring that their research findings are both substantive and accessible.The article by Gary King, Michael Tomz, and Jason Wittenberg from Harvard University addresses the common issue in social science research where statistical results are not fully utilized, leading to missed opportunities to present meaningful quantities and express appropriate uncertainty. They propose a method based on statistical simulation to extract and present overlooked information in a reader-friendly manner. The authors argue that this approach can enrich the substance of social science research, improve the candor and realism of statistical discourse, and make results accessible to a broader audience. The article outlines the problem of statistical interpretation, emphasizing the need to go beyond simple interpretations and statistical significance to convey substantive quantities. It introduces the concept of simulation as a tool to compute quantities of interest and their uncertainties, providing algorithms for predicting values, expected values, and first differences. The authors also discuss alternative methods such as Bayesian techniques, bootstrapping, and analytical approaches like the delta method, highlighting their advantages and limitations. To illustrate their approach, the authors replicate and enhance the interpretations of several published works, showing how their methods can reveal new insights and improve the clarity and precision of statistical results. They also provide an easy-to-use software package called "CLARIFY" to implement their suggestions, which has won awards for its effectiveness and ease of use. Overall, the article aims to guide social scientists in making more informative and transparent use of statistical data, ensuring that their research findings are both substantive and accessible.