What does normalization of ratings data adjust for?

Normalization of ratings data specifically adjusts for values that are measured on different scales. When different datasets have varying ranges or units of measurement, normalization allows for a standard comparison by transforming those values into a common scale. This is crucial when aggregating or comparing data from multiple sources, as it ensures that the variations in scale do not skew the analysis or interpretations of the ratings.

For instance, if one dataset rates a performance on a scale of 1 to 10, while another uses a scale of 1 to 100, normalization enables a more equitable comparison by bringing those values to a uniform scale. This process helps maintain the integrity of the data analysis and allows for more meaningful insights to be drawn from the ratings.

The other options do not accurately capture the purpose of normalization. Measuring only on a single scale doesn’t address the variability present in the data, while averaging ratings without modifications ignores the underlying differences in scale. Eliminating outliers is a separate process that pertains to adjusting the dataset rather than standardizing values across different scales.