Ensuring data quality is paramount. Data cleaning involves checking for and correcting errors, inconsistencies, and duplicate entries. Handling missing data is another critical aspect. Techniques such as multiple imputation, maximum likelihood estimation, and using algorithms like Expectation-Maximization (EM) are commonly employed to address this issue, ensuring that the analysis remains robust and unbiased.
