Erroneous data (also known as “bad data”) is data that is incorrectly entered or otherwise rendered incorrect. The incorrectness can derive from any number of sources, such as incorrectly measured or incorrectly entered values, data which is out of context or selectively presented, or simply incorrect measurements.
Erroneous data can lead to inaccurate results when used in calculations, skewed reports, incorrect diagnoses, and other issues depending on the application. To avoid such situations, data quality checks can be performed to identify and remove any invalid or unclean data sets, as well as ensure accuracy over time through data mining and other quality control techniques. Data analysts must also be careful to ensure that policies and procedures are in place to ensure accurate data entry.
In short, it is important to recognize erroneous data and understand how it can be prevented or corrected. Quality control and data validation are key components for any system or organization, as they can help to ensure that correct results are obtained from any analysis performed or decisions made.
