Methoddependent

Methoddependent is an adjective used to describe results, conclusions, or measurements that vary depending on the method used to obtain them. It signals that the observed outcome is not solely a property of the underlying phenomenon, but also a product of choices in data collection, processing, modeling, or analysis.

Causes of methoddependence include differences in experimental or sampling design, data preprocessing and cleaning, model specification and assumptions, estimator or algorithm choices, and parameter settings. In statistics, different estimators can yield distinct point estimates or intervals; in data science, pipelines for feature extraction and normalization can shift results; in scientific measurement, calibration and interpretation choices can alter conclusions. The phenomenon is common when data are complex, noisy, or when multiple reasonable approaches exist to address a question.

Implications center on reproducibility and comparability. Methoddependent results require careful documentation of all methodological choices and transparent reporting of how conclusions would change under alternative reasonable methods. Mitigation strategies include conducting sensitivity analyses across multiple methods, preregistering analysis plans when feasible, performing cross-method validation, and sharing data and code openly. Emphasizing robustness and using method-agnostic summaries when possible can also help ensure that inferences remain credible even if specific methods evolve.