What is the primary outcome of quantifying variance in a dataset?

Quantifying variance in a dataset is essential because it provides a measure of the spread or dispersion of the data points around the mean. Variance indicates how much the individual data points differ from the mean value of the dataset. A high variance suggests that the data points are spread out over a wider range of values, while a low variance indicates that they are clustered closely around the mean.

Understanding variance is crucial in statistics because it helps in assessing the reliability of the mean as a measure of central tendency. Without an understanding of how dispersed the data points are, one cannot accurately interpret the mean or understand the overall variability within the dataset. This makes variance a fundamental concept when analyzing data, as it allows statisticians to understand not just the average values but also the variation present in those values.