Nullverdier

Nullverdier is a term used in data management to denote missing or undefined data within a dataset. They indicate that a value has not been observed, is inapplicable, or is unknown. The concept is universal across databases, spreadsheets, and analytics tools, though the terminology varies by platform: NULL in SQL, NaN in floating-point contexts, NA in R, None in Python, and empty string or zero are not interchangeable with nulls.

Interpretation and semantics: Nullverdier can reflect different states; some systems treat NULL as unknown and others as inapplicable. In many query languages, comparisons involving a null are not true or false but unknown, enabling three-valued logic. Writing and reading data with null values requires careful handling to avoid misinterpretation.

Impact on analysis: Missing values can bias results, reduce statistical power, or break computations. They may necessitate data cleaning, imputation, or models that can accommodate missingness. The pattern of missingness is categorized in statistical practice as MCAR (missing completely at random), MAR (missing at random), or MNAR (missing not at random).

Handling strategies: Common approaches include removing records with null values (listwise deletion), imputing plausible values (mean, median, regression, k-nearest neighbors, or multiple imputation), or modeling missingness as a separate feature. In databases, constraints and defaults can prevent unintended nulls; some systems use sentinel values when a value is truly inapplicable.

In practice, the treatment of nullverdier depends on the analysis objective and the data context. Transparent reporting of how missing data are handled is standard in data-driven work.