The association between adolescent well-being and digital technology use is examined using Specification Curve Analysis (SCA) across three large-scale datasets (n = 355,358). The study finds a small negative correlation between digital technology use and adolescent well-being, explaining at most 0.4% of the variation in well-being. However, the effects are too small to warrant policy changes. The analysis highlights methodological challenges in large-scale datasets, including researcher degrees of freedom and potential false positives. The study also compares technology use effects with other activities, finding that factors like sleep, eating breakfast, and avoiding bullying have stronger associations with well-being. The results suggest that digital technology use has a minimal impact on adolescent well-being, and that the emphasis on screen time in public discourse may be unwarranted. The study emphasizes the need for transparent and robust analytic practices, and the importance of considering control variables and common method variance. The findings underscore the complexity of the relationship between digital technology use and well-being, and the need for further research to understand the true impact of technology on adolescent mental health. The study also highlights the limitations of using simple linear regressions and the importance of using comprehensive, high-quality data for accurate analysis. Overall, the research provides a nuanced understanding of the relationship between digital technology use and adolescent well-being, emphasizing the need for careful interpretation of large-scale data in behavioral science.The association between adolescent well-being and digital technology use is examined using Specification Curve Analysis (SCA) across three large-scale datasets (n = 355,358). The study finds a small negative correlation between digital technology use and adolescent well-being, explaining at most 0.4% of the variation in well-being. However, the effects are too small to warrant policy changes. The analysis highlights methodological challenges in large-scale datasets, including researcher degrees of freedom and potential false positives. The study also compares technology use effects with other activities, finding that factors like sleep, eating breakfast, and avoiding bullying have stronger associations with well-being. The results suggest that digital technology use has a minimal impact on adolescent well-being, and that the emphasis on screen time in public discourse may be unwarranted. The study emphasizes the need for transparent and robust analytic practices, and the importance of considering control variables and common method variance. The findings underscore the complexity of the relationship between digital technology use and well-being, and the need for further research to understand the true impact of technology on adolescent mental health. The study also highlights the limitations of using simple linear regressions and the importance of using comprehensive, high-quality data for accurate analysis. Overall, the research provides a nuanced understanding of the relationship between digital technology use and adolescent well-being, emphasizing the need for careful interpretation of large-scale data in behavioral science.