During the data analysis process, which step is crucial for ensuring data accuracy before any modeling or interpretation?

Explanation: Data cleaning is critical to the data analysis process as it ensures the accuracy and reliability of the results. Cleaning involves identifying and correcting errors, removing duplicates, and handling missing values. Without this step, subsequent analysis may lead to incorrect conclusions or biased models. For example, if sales data has duplicate entries, the total revenue figure might be inflated. Cleaning ensures that the dataset reflects reality and forms a robust foundation for exploration, modeling, and interpretation. Option A: Data collection is the initial step but does not address inaccuracies inherent in raw data. It only provides the dataset for subsequent steps. Option C: Data visualization is a presentation step used to interpret results, not to ensure accuracy. Option D: Model training uses clean data to develop predictive models but does not address data quality issues directly. Option E: Hypothesis testing comes at a later stage, relying on clean data for meaningful statistical conclusions.