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,

While calls to reduce class size in school have considerable popular appeal, the related discussion of the scientific evidence has been limited and highly selective. The evidence about improvements in student achievement that can be attributed to smaller classes turns out to be meager and unconvincing. In the aggregate, pupil-teacher ratios have fallen dramatically for decades, but student performance has not improved. Explanations for these aggregate trends, including more poorly prepared students and the influence of special education, are insufficient to rationalize the overall patterns. International comparisons fail to show any significant improvements from having smaller pupil-teacher ratios. Detailed econometric evidence about the determinants of student performance confirms the general lack of any achievement results from smaller classes. Finally, widely cited experimental evidence actually offers little support for general reductions in class size. In sum, while policies to re...

This paper investigates the extent and ways in which childhood school quality factors causally influence later-life health outcomes. The study analyzes the health trajectories of children born between 1950 and 1975, and followed through 2007, using the Panel Study of Income Dynamics (PSID), spanning four decades linked with multiple data sources containing detailed neighborhood attributes and school quality resources that prevailed at the time these children were growing up. I estimate the long-run impacts of court-ordered school desegregation plans on later-life health by exploiting quasi-random variation in the timing and scope of desegregation implementation during the 1960s, 70s, and 80s. I find school desegregation significantly narrowed black-white adult health disparities for the cohorts exposed to integrated schools during childhood. The analysis disentangles the effects of neighborhood and school quality. Difference-in-differences estimates and sibling-difference estimates indicate that school desegregation and accompanied increases in school quality resulted in

Brunswick and Director of the Canadian Research Institute for Social Policy. We are grateful to four anonymous referees for their comments on an earlier draft. The views expressed herein are solely our own. 1

Significance testing as used has no theoretical justification. Our article in the Journal of Economic Literature (1996) showed that of the 182 full-length papers published in the 1980s in the American Economic and Review 70 % did not distinguish economic from statistical significance. Since 1996 many colleagues have told us that practice has improved. We interpret their response as an empirical claim, a judgment about a fact. Our colleagues, unhappily, are mistaken: significance testing is getting worse. We find here that in the next decade, the 1990s, of the 137 papers using a test of statistical significance in the AER fully 82 % mistook a merely statistically significant finding for an economically significant finding. A super majority (81%) believed that looking at the sign of a coefficient sufficed for science, ignoring size. The mistake is causing economic damage: losses of jobs and justice, and indeed of human lives (especially in, to mention another field enchanted with statistical significance as against substantive significance, medical science). The confusion between fit and importance is causing false hypotheses to be accepted and true hypotheses to be rejected. We propose a publication standard for the future: “Tell me the oomph of your coefficient; and do not confuse it with merely statistical significance.”