In this article, we present a Bayes factor solution for inference in multiple regression. Bayes factors are principled measures of the relative evidence from data for various models or positions, including models that embed null hypotheses. In this regard, they may be used to state positive evidence for a lack of an effect, which is not possible in conventional significance testing. One obstacle to the adoption of Bayes factor in psychological science is a lack of guidance and software. Recently, Liang, Paulo, Molina, Clyde, and Berger (2008) Liang, F., Paulo, R., Molina, G., Clyde, M. A. and Berger, J. O. 2008. Mixtures of g-priors for {B}ayesian variable selection. Journal of the American Statistical Association, 103: 410–423. Retrieved from http://pubs.amstat.org/doi/pdf/10.1198/016214507000001337[Taylor & Francis Online], [Web of Science ®] , [Google Scholar] developed computationally attractive default Bayes factors for multiple regression designs. We provide a web applet for convenient computation and guidance and context for use of these priors. We discuss the interpretation and advantages of the advocated Bayes factor evidence measures.