Critical Inquiry Exam 2 Practice

Data cleaning is the process of identifying and correcting errors, handling missing values, and resolving inconsistencies in a dataset before analysis. This step is essential because errors, gaps, and conflicting information can distort results, lead to biased conclusions, and undermine the reliability of any model or decision based on the data. By cleaning the data, you ensure that analyses reflect true patterns rather than artifacts of messy data, which improves accuracy and trust in findings. The option describing identifying and correcting errors, missing values, and inconsistencies best captures this preparation. Collecting more data doesn’t fix quality issues, removing all missing values can waste information or bias results, and analyzing without processing skips necessary quality checks.