What does data normalization achieve in a dataset?

Data normalization primarily achieves the adjustment of values to a common scale, which facilitates better comparisons among different features in a dataset. When working with data that encompasses various attributes or features, these attributes can have different units or scales. For instance, one feature might be measured in dollars, while another could be in percentages. Normalization transforms these values into a standard range, typically between 0 and 1 or -1 and 1, enabling analysts and models to interpret and compare the data effectively.

This process helps improve the performance and accuracy of machine learning algorithms, which often assume that all input features contribute equally to the result. By normalizing the dataset, it ensures that no single feature disproportionately influences the analysis or modeling outcomes due to its scale.

The other options do not accurately represent the goals or effects of normalization. Distorting differences in value ranges, increasing dataset volume, or focusing solely on outliers do not capture the essence of what normalization accomplishes in data processing and analysis.