What term describes incorrect or missing values in a dataset?

The term that describes incorrect or missing values in a dataset is "Errors." In data science, errors refer specifically to inaccuracies or anomalies that may occur for various reasons, such as data entry mistakes, sensor malfunctions, or incomplete data collection. These inaccuracies can significantly affect the analysis and modeling, as they can lead to incorrect conclusions or predictions.

Understanding this term is crucial because identifying and managing errors in a dataset is a fundamental step in the data preparation process, ensuring the quality and reliability of the data used for analysis. While some may consider terms like "Invalid Entries" or "Noise," these are more specific or broader concepts. Invalid entries typically pertain to data that does not conform to the expected format or criteria, while noise refers to random variations or irrelevant data that can obscure meaningful patterns. Outliers, on the other hand, are observations that lie outside the expected range, which may or may not be indicative of errors. Therefore, "Errors" is the most accurate term to encompass incorrect or missing values within the dataset.