Experimental field trials are a powerful means to advance behavioral research. This is especially urgent in the sustainability context, where causal conclusions are needed about the effects of real-world interventions on individual and collective responses such as energy or water consumption. Behavioral measurements can complement traditional survey-based approaches - however, unique challenges and sources of scientific error are posed by both collection procedures and the multidimensional nature of the data.
Drawing on insights from >10 experimental field trials conducted across three Horizon 2020 projects, we adapt the original Total Survey Error (TSE) framework developed for survey methodology to provide a comprehensive approach to systematically identify, categorize, and mitigate sources of error in (quasi-)experimental field trials. We illustrate how researchers can proactively design studies that balance internal and external validity while maintaining practical feasibility, examining each stage of the research process—from design and data collection to processing and interpretation. Our framework highlights key sources of scientific error in four main areas: measurement, sampling, experimental implementation, and data engagement. We underscore the importance of providing a standardized terminology, and suggest tailored strategies to address the defined errors based on experience from our previously conducted field trials. This roadmap for conducting high-quality experimental field trials will allow for more robust and actionable behavioral insights.