What do we call irrelevant or irregular data values that obscure meaningful patterns in other relevant data?

The term for irrelevant or irregular data values that obscure meaningful patterns is "noise." Noise in data refers to random errors or variances in measured variables that do not reflect the true values. This extraneous information can interfere with the analysis and interpretation of the data, making it difficult to discern actual trends, patterns, or relationships that are significant to the analysis.

Understanding noise is crucial in data science because it affects the quality of the data. In practical applications, noise can arise from various factors such as measurement errors, environmental changes, or human error during data collection. Distinguishing noise from relevant signals is essential for building effective models and deriving accurate conclusions from the data.

While options like variance, outliers, and bias relate to data characteristics and influence analysis, they do not specifically pertain to the broader concept of noise. Variance deals with the spread of data points, outliers refer to extreme values that deviate from the general distribution, and bias indicates systematic errors that skew results in one direction. Therefore, noise captures the essence of irrelevant or irregular data values that hinder understanding meaningful data patterns, reinforcing the appropriateness of this choice.