Analysefeil

Analysefeil, Norwegian for "analysis error," refers to errors in reasoning, methods, or data interpretation that lead to incorrect conclusions after analysing information. It is a broad concept that can affect scientific research, journalism, data science, and policy-making.

Common forms of analysefeil include misinterpreting correlation as causation, inappropriate handling of missing data, selection bias, measurement error, model mis-specification, p-hacking or data-dredging, overfitting or underfitting, and errors arising from multiple testing or data snooping. These faults can occur at any stage of analysis, from data collection and cleaning to model selection and result interpretation, and they often produce results that seem convincing but are not replicable or generalizable.

Causes of analysefeil include flawed study design, inadequate data quality, and the use of inappropriate or misapplied statistical methods. Cognitive biases, such as confirmation bias and motivated reasoning, as well as external pressures like publication incentives or political considerations, can also contribute to erroneous analyses. Insufficient transparency in reporting methods and data further complicates the identification of errors.

Detection and prevention rely on rigorous methodological practices. Preregistration of study plans, robust statistical methods, and proper handling of data and missing values help reduce errors. Techniques such as cross-validation, sensitivity analyses, and robust replication efforts enhance reliability. Transparent reporting, sharing of data and code, and independent peer review are important safeguards.

In practice, analysefeil highlights the need for cautious interpretation of results and rigorous methodology across research domains, ensuring conclusions are supported by sound evidence and reproducible analyses.