What does 'cleaning a dataset' involve?

Cleaning a dataset is a critical process in data management that focuses on improving the quality and usability of the data. This involves several tasks aimed at ensuring that the data is accurate, consistent, and trustworthy for analysis. The main activities in data cleaning include removing duplicates—instances where the same data point may be recorded more than once—and fixing errors, which can include correcting typos, adjusting incorrect entries, and handling missing data points.

By addressing these issues, the dataset becomes more reliable and ready for analysis, allowing for better decision-making and insights. When data is clean, it significantly reduces the likelihood of errors in results and enhances the overall integrity of any analysis performed on the dataset. Hence, cleaning a dataset directly relates to the meticulous processes of removing duplicates and correcting errors, making this choice an accurate reflection of what data cleaning entails.