The influential claim that most published results are false raised concerns about the trustworthiness and integrity of science. Since then, there have been numerous attempts to examine the rate of false-positive results that have failed to settle this question empirically. Here we propose a new way to estimate the false positive risk and apply the method to the results of (randomized) clinical trials in top medical journals. Contrary to claims that most published results are false, we find that the traditional significance criterion of $\alpha = .05$ produces a false positive risk of 13%. Adjusting $\alpha$ to .01 lowers the false positive risk to less than 5%. However, our method does provide clear evidence of publication bias that leads to inflated effect size estimates. These results provide a solid empirical foundation for evaluations of the trustworthiness of medical research.

翻译：有影响力的观点认为，大多数已发表的研究结果是不真实的，这引发了人们对科学可信度和诚信的担忧。此后，众多研究尝试检验假阳性结果的比例，但未能从实证角度解决这一问题。本文提出一种估算假阳性风险的新方法，并将其应用于顶级医学期刊中（随机）临床试验的结果分析。与“大多数发表结果不真实”的观点相反，我们发现传统显著性标准$\alpha = .05$会带来13%的假阳性风险；将$\alpha$调整至.01可使假阳性风险降至5%以下。然而，我们的方法明确揭示了会导致效应量估计值膨胀的发表偏倚。这些结果为评估医学研究的可信度提供了坚实的实证基础。