Social scientists rarely take full advantage of the information available in their statistical results. As a consequence, they miss opportunities to present quantities that are of greatest substantive interest for their research and express the appropriate degree of certainty about these quantities. In this article, we offer an approach, built on the technique of statistical simulation, to extract the currently overlooked information from any statistical method and to interpret and present it in a reader-friendly manner. Using this technique requires some expertise,

The last two decades have witnessed an explosion in the use of computationally intensive methodologies in the social sciences as computer technology has advanced. Among these empirical methods are Maximum Likelihood (ML) procedures. ML estimators possess a variety of desirable qualities, perhaps most prominent of which is the asymptotic efficiency of the standard errors. However, the behavior of the estimators in general, of the estimates of the standard errors in particular, and thus of inferential hypothesis tests are uncertain in small sample analyses. In political science research, small samples are routinely the subject of empirical investigation using ML methods, yet little is known regarding what effect sample size has on a researcher’s ability to draw inferences This paper explores the behavior of ML estimates in probit models across differing sample sizes and with varying numbers of independent variables in Monte Carlo simulations. Our experimental results