What does data cleaning involve in the context of data analysis?

Data cleaning is a critical step in the data analysis process, focusing specifically on ensuring the accuracy and quality of the data that will be analyzed. This process includes identifying and correcting or removing records that are inaccurate, incomplete, or irrelevant. Inaccurate data can lead to misleading results, so good data cleaning practices help to enhance data integrity, making analyses more reliable and valid.

The act of correcting inaccuracies might involve standardizing formats, fixing typos, filling in missing values, or removing duplicate entries. By focusing on the quality of data through cleaning, analysts can ensure that subsequent analyses yield meaningful insights that truly reflect the underlying phenomena being studied.

The other options pertain to different aspects of data management or analysis. Collecting data is essential but precedes the cleaning process. Visualizing trends relates to presenting data for insights, while storing data speaks to data management practices, both of which occur after data has been cleaned and validated.