datafortolkning

Datafortolkning is the process of deriving meaning from data by translating numerical or categorical outputs into conclusions, explanations, or actions. It combines statistical analysis, computational methods, and domain knowledge to extract insights and inform decisions. It follows data collection and preprocessing and results in statements about what the data suggests about a question or phenomenon. In Danish and Norwegian usage, the term commonly refers to this interpretive step within analytics and research.

The interpretation depends on the questions asked, the methods used, and the context. Analysts assess uncertainty, data quality, sampling bias, and model assumptions. They translate results into actionable conclusions, noting limitations and the confidence in findings. Transfer to practice involves communicating with non-technical audiences so that decisions can be grounded in what the data actually indicate.

Methods include descriptive statistics, hypothesis testing, regression, forecasting, visualization, and machine learning outputs. Visualization helps reveal patterns but can also mislead if not designed carefully. It is important to report measures of uncertainty (for example, confidence intervals) and to consider alternative explanations.

Challenges include bias, p-hacking, data dredging, overfitting, and misinterpretation of correlation as causation. Ethical considerations include privacy, consent, representativeness, and potential harms of decisions based on data. Effective datafortolkning requires transparency about assumptions and limitations to support trustworthy conclusions.

In practice, datafortolkning is a collaborative activity among data scientists, subject-matter experts, and decision-makers. It requires data literacy, clear communication, and a transparent workflow to enable reproducibility and trust.