A commitment to developing methodologically rigorous evidence has long been a part of social work research. Currently, there is an explosion of new ideas in statistics about how the strongest evidence may be generated. Advances in areas as diverse as survival analysis, multilevel modeling, Bayesian reasoning and decision making, meta-analysis and text-mining, are continuously adding to the methodological toolkit of those concerned about addressing social problems. Discussions within the field of social work sometimes seem to characterize the finer points of statistical and quantitative methods as arcane and having possibly little to do with pragmatic realities of policy and practice. To the contrary, ample examples suggest that misinterpretations of seemingly technical aspects of statistical reasoning actually have very practical implications across a wide variety of fields ranging from welfare reform, to voting patterns, to the diagnosis of mental and physical disease. In short, failure to account for the nuances of statistical reasoning in some cases leads to practical conclusions that are quite simply substantively wrong. Further, use of advanced quantitative methods may afford insights about processes and connections that are not discernible with simpler and more traditional statistical methods. Other areas of social research, such as psychology, economics and public health are already embracing new tools and ideas in quantitative reasoning. How can social work embrace these ideas and continue to maintain a seat at the decision making table? How can social work continue to contribute to a stronger evidence base for policy and practice that helps the most vulnerable?