The Two Metrics That Reveal True Data Dispersion Beyond Standard Deviation
We’ve all heard the saying, “Variety is the spice of life,” and in data, that variety or diversity often takes the form of dispersion.
Data dispersion makes data fascinating by highlighting patterns and insights we wouldn’t have found otherwise. Typically, we use the following as measures of dispersion: variance, standard deviation, range, and interquartile range (IQR). However, we may need to examine dataset dispersion beyond these typical measures in some cases.
Whether we’re measuring people’s heights or housing prices, we seldom find all data points to be the same. We won’t expect everyone to be the same. Some people are tall, average, or short. The data generally varies. In order to study this data variability or dispersion, we usually quantify it using measures like range, variance, standard deviation, etc. The measures of dispersion quantify how spread out our data points are.
However, what if we wish to evaluate the variability across datasets? For example, what if we want to compare the sales prices of a jewelry shop and a bookstore? Standard deviation won’t work here, as the scales of the two datasets are likely very different.
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