What is the purpose of data normalization?

The purpose of data normalization is primarily to ensure data consistency across different scales, making it easier to compare and analyze datasets that may have varying ranges and units of measurement. By standardizing the data, normalization helps to bring all attributes to a similar scale without distorting differences in the ranges of values.

This is particularly important in statistical analyses and machine learning, where features with larger ranges can disproportionately influence the results. Normalization techniques, such as min-max scaling and z-score normalization, adjust the values systematically, which in turn facilitates more effective modeling, enhances the performance of algorithms, and leads to more valid statistical conclusions.

In contrast, increasing the size of the dataset, visualizing data effectively, or filtering out insignificant data points do not capture the core intent behind data normalization. Each of these processes serves different objectives and thus does not align with the primary purpose of establishing consistency in data scales.