ABSTRACT Given the competition for top journal space, there is an incentive to produce “significant” results. With the combination of unreported tests, lack of adjustment for multiple tests, and direct and indirect p‐hacking, many of the results being published will fail to hold up in the future. In addition, there are basic issues with the interpretation of statistical significance. Increasing thresholds may be necessary, but still may not be sufficient: if the effect being studied is rare, even t > 3 will produce a large number of false positives. Here I explore the meaning and limitations of a p‐value. I offer a simple alternative (the minimum Bayes factor). I present guidelines for a robust, transparent research culture in financial economics. Finally, I offer some thoughts on the importance of risk‐taking (from the perspective of authors and editors) to advance our field. SUMMARY Empirical research in financial economics relies too much on p‐values, which are poorly understood in the first place. Journals want to publish papers with positive results and this incentivizes researchers to engage in data mining and “p‐hacking.” The outcome will likely be an embarrassing number of false positives—effects that will not be repeated in the future. The minimum Bayes factor (which is a function of the p‐value) combined with prior odds provides a simple solution that can be reported alongside the usual p‐value. The Bayesianized p‐value answers the question: What is the probability that the null is true? The same technique can be used to answer: What threshold of t‐statistic do I need so that there is only a 5% chance that the null is true? The threshold depends on the economic plausibility of the hypothesis.