Statistical Deviation


Statistical deviation refers to the measure of the amount of variation or dispersion in a dataset from its mean or central value. It provides insights into how individual data points differ from the average.


To calculate the statistical deviation, you need to follow these steps:

  1. Calculate the mean (average) of the dataset.
  2. Subtract the mean from each data point individually.
  3. Square each deviation obtained in the previous step.
  4. Sum up all the squared deviations.
  5. Divide the sum by the total number of data points.
  6. Take the square root of the quotient to obtain the standard deviation.


The statistical deviation provides a quantitative measure of how spread out or clustered the data points are around the mean. A high deviation indicates greater variability or dispersion, while a low deviation suggests that the data points are closely grouped together.


The statistical deviation has several applications, including:

  • Assessing the spread of data in finance, economics, and investing.
  • Examining the reliability and consistency of measurements in scientific research.
  • Identifying outliers or unusual data points that may affect the overall analysis.
  • Comparing the variability between different sets of data.


It is important to keep in mind the following limitations when using statistical deviation:

  • Deviation alone does not provide a complete picture of the distribution of data.
  • It assumes a normal distribution of data, which may not always be the case.
  • In datasets with extreme outliers, the deviation may be significantly influenced.
  • Statistical deviation can be sensitive to changes in the dataset size or extreme values.