The article discusses the interpretation of non-significant results in scientific research, emphasizing the limitations of traditional methods such as power and confidence intervals. It introduces Bayes factors as a more coherent and flexible approach to evaluating non-significant results. Bayes factors compare the relative evidence for two theories, allowing researchers to determine whether the data support one theory over another or indicate insensitivity. The article provides detailed examples and explanations of how to calculate Bayes factors using a simple online calculator, highlighting its advantages over traditional methods. It also addresses the challenges of specifying minimal interesting values and the importance of aligning theoretical predictions with the data. The author argues that Bayes factors provide a more robust framework for interpreting non-significant results, enabling researchers to make more informed decisions about their theories.The article discusses the interpretation of non-significant results in scientific research, emphasizing the limitations of traditional methods such as power and confidence intervals. It introduces Bayes factors as a more coherent and flexible approach to evaluating non-significant results. Bayes factors compare the relative evidence for two theories, allowing researchers to determine whether the data support one theory over another or indicate insensitivity. The article provides detailed examples and explanations of how to calculate Bayes factors using a simple online calculator, highlighting its advantages over traditional methods. It also addresses the challenges of specifying minimal interesting values and the importance of aligning theoretical predictions with the data. The author argues that Bayes factors provide a more robust framework for interpreting non-significant results, enabling researchers to make more informed decisions about their theories.