We develop an estimator that allows us to calculate an upper bound to the fraction of unrejected null hypotheses tested in economics journal articles that are in fact true. Our point estimate is that none of the unrejected nulls in our sample is true. We reject the hypothesis that more than one-third are true. We consider three explanations for this finding: that all null hypotheses are mere approximations, that data-mining biases reported standard errors downward, and that journals tend to publish papers that fail to reject their null hypotheses only when the null hypotheses are likely to be false. While all these explanations are important, the last seems best able to explain our findings.

We develop an estimator that allows us to calculate an upper bound to the fraction of unrejected null hypotheses tested in economics journal articles that are in fact true. Our point estimate is that none of the unrejected nulls in our sample is true. We reject the hypothesis that more than one-third are true. We consider three explanations for this finding: that all null hypotheses are mere approximations, that data-mining biases reported standard errors downward, and that journals tend to publish papers that fail to reject their null hypotheses only when the null hypotheses are likely to be false. While all these explanations are important, the last seems best able to explain our findings.