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Measures of Variability

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So far, we have discussed measures of central tendency (where the data is concentrated) and measures of asymmetry (how the data is spread) The third dimension to assess a data set is variability. It broadly tells us the degree to which data points are dispersed from the Mean and each other.

This dimension is important in making any predictions using a given data set. A higher variability decreases one's ability to confidently predict future values, whereas a lower one allows one to predict the same with confidence.

Of the many ways to measure variability, we will focus on the following in the next sections:

  • Variance
  • Standard Deviation
  • Coefficient of Variation

Other measures include:

  • Range - the difference between the highest and lowest values in a data set
  • Inter quartile range - this is the range of the middle half of the data set. To calculate this, you would split the data into four equal parts, as shown below and get the difference between Q3 and Q1.