Gain-probability diagrams as an alternative to significance testing in economics and finance

Purpose - The purpose of this article is to show the gains that can be made if researchers were to use gain-probability (G-P) diagrams. Design/methodology/approach - The authors present relevant mathematical equations, invented examples and real data examples. Findings - G-P diagrams provide a more nuanced understanding of the data than typical summary statistics, effect sizes or significance tests. Practical implications - Gain-probability diagrams provided a much better basis for making decisions than typical summary statistics, effect sizes or significance tests. Originality/value - G-P diagrams provide a completely new way to traverse the distance from data to decision-making implications.