Empirical results often hinge on data analytic decisions that are simultaneously defensible, arbitrary, and motivated. To mitigate this problem we introduce Specification-Curve Analysis. This approach consists of three steps: (i) estimating the full set of theoretically justified, statistically valid, and non-redundant analytic specifications, (ii) displaying the results graphically in a manner that allows identifying which analytic decisions produce different results, and (iii) conducting statistical tests to determine whether the full set of results is inconsistent with the null hypothesis of no effect. We illustrate its use by applying it to three published findings. One proves robust, one weak, one not robust at all. Although it is impossible to eliminate subjectivity in data analysis, Specification-Curve Analysis minimizes the impact of subjectivity on the reporting of results, resulting in a more systematic, thorough, and objective presentation of the data.