Why might data be removed during the cleaning phase of a dataset?

Data removal during the cleaning phase of a dataset primarily addresses issues of accuracy, relevance, duplication, and completeness. This process is crucial for ensuring that the dataset is of high quality, which directly impacts the outcomes of any analysis performed on it.

Inaccurate data can lead to misleading results or faulty conclusions, which is why cleaning efforts often focus on identifying and eliminating such entries. Irrelevant data—information that does not contribute to the analysis objectives—can also skew results and create noise that obscures meaningful patterns. Duplicated entries can inflate the significance of certain findings and distort statistical analysis, thus compromising the integrity of conclusions drawn from the dataset. Similarly, incomplete data can lead to gaps in analysis, making it difficult to understand the full picture.

The goal of the cleaning phase is to refine the dataset so that it accurately reflects the reality it is intended to represent, thereby enhancing the overall quality and reliability of the analysis.