Plausibilitätschecks

Plausibilitätschecks, also known as plausibility checks or validation checks, are processes used to assess the reasonableness and consistency of data or information. The goal is to identify and flag potential errors, inconsistencies, or illogical values before they are used in further analysis, decision-making, or processing.

These checks can be applied in various contexts, including data entry, software development, scientific research, and business analytics. They operate on the principle that data or outcomes should conform to expected patterns, rules, or common sense. For instance, a plausibility check might verify if a person's age is a realistic value (e.g., not negative or excessively high), if a date falls within an expected range, or if a calculated result aligns with known physical laws or business logic.

The implementation of plausibility checks can range from simple rule-based validations to more complex statistical analyses. Simple checks might involve range checks, format checks, or type checks. More advanced checks could involve cross-referencing data from different sources, identifying outliers, or comparing current data to historical trends. When a plausibility check fails, it indicates a potential issue that requires investigation. This might involve correcting erroneous data, revising algorithms, or re-evaluating assumptions. Ultimately, plausibility checks are a crucial step in ensuring data quality and reliability.