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Data preparation is a crucial step in the data analysis process where raw data is transformed into a format that is suitable for analysis. One of its primary functions is to reduce errors. This involves cleaning the data by identifying and correcting inaccuracies, inconsistencies, and missing values. By ensuring that the data is accurate and reliable, analysts can draw more valid conclusions from their analyses.
Reducing errors during data preparation helps improve the overall quality of the data, which is essential for making informed business decisions. High-quality data enables better insights, enhances the reliability of the analysis, and supports effective strategy formulation. Inaccurate data could lead to misleading conclusions, making the reduction of errors a fundamental aspect of preparing data for meaningful analysis.
The other choices do not capture the essence of data preparation as effectively. Increasing sample size may be a goal of research but does not pertain directly to the function of preparing existing data. Enhancing data complexity contradicts the aim of simplifying data for analysis, and creating new data measurements may occur post-analysis rather than during the preparation phase.