Fehlwert

Fehlwert is a term used in data processing, statistics and measurements to denote a value that is missing, invalid, or unusable for analysis. It can arise in numerical datasets, survey responses, sensor readings or experimental results when data are not observed, not recorded, corrupted, or fall outside acceptable ranges.

Missing values can occur for various reasons, such as nonresponse in surveys, sensor or instrument failure, data entry errors, or deliberate masking for privacy. In statistical discussions, missing data are sometimes categorized by the mechanism that caused them, for example as missing completely at random, missing at random, or missing not at random, which influences the choice of handling method.

Fehlwerts are commonly represented by special placeholders in data stores, such as NULL, NaN (not a number), or empty cells. Proper handling requires explicit documentation of the extent and nature of Fehlwerts, since their presence can bias results, distort variance estimates, and reduce statistical power if ignored or mishandled.

Common strategies to manage Fehlwerts include deletion of incomplete cases (listwise or pairwise), imputation (replacing missing values with estimates such as mean, median, or model-based predictions), and model-based approaches that can incorporate missingness into the analysis. More advanced techniques include multiple imputation and algorithms that tolerate missing data.

In practice, transparent reporting of missing data, the chosen handling method, and sensitivity analyses are essential to maintain data quality and validity of conclusions.