This article takes issue with a recent book by Ziliak and McCloskey (2008) of the same title. Ziliak and McCloskey argue that statistical significance testing is a barrier rather than a booster for empirical research in many fields and should therefore be abandoned altogether. The present...

In this paper, we discuss the importance of sample size in the evaluation of minimum wage effects. We first show which sample sizes are necessary to make reliable statements about the effects of minimum wages on binary outcomes, and second how to determine these sample sizes. This is...

In this paper, we discuss the importance of sample size in the evaluation of minimum wage effects. We first show which sample sizes are necessary to make reliable statements about the effects of minimum wages on binary outcomes, and second how to determine these sample sizes. This is...

Summary Significance tests were originally developed to enable more objective evaluations of research results. Yet the strong orientation towards statistical significance encourages biased results, a phenomenon termed “publication bias”. Publication bias occurs whenever the likelihood or...

Empirical growth regressions typically include mean years of schooling as a proxy for human capital. However, empirical research often finds that the sign and significance of schooling depends on the sample of observations or the specification of the model. We use a nonparametric local-linear...

In this paper, we discuss the importance of sample size in the evaluation of minimum wage effects. We first show which sample sizes are necessary to make reliable statements about the effects of minimum wages on binary outcomes, and second how to determine these sample sizes. This is...

In this paper, we discuss the importance of sample size in the evaluation of minimum wage effects. We first show which sample sizes are necessary to make reliable statements about the effects of minimum wages on binary outcomes, and second how to determine these sample sizes. This is...