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  1. Introduction
  2. Graphing Distributions

  3. Summarizing Distributions
    1. Contents
      Standard
    2. Central Tendency
      Standard         Video
    3. What is Central Tendency
      Standard         Video
    4. Measures of Central Tendency
      Standard         Video
    5. Balance Scale Simulation
      Standard         Video
    6. Absolute Differences Simulation
      Standard
    7. Squared Differences Simulation
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    8. Median and Mean
      Standard         Video
    9. Mean and Median Demo
      Standard
    10. Additional Measures
      Standard         Video
    11. Comparing Measures
      Standard         Video
    12. Variability
      Standard  
    13. Measures of Variability
      Standard         Video
    14. Variability Demo
      Standard
    15. Estimating Variance Simulation
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    16. Shapes of Distributions
      Standard         Video
    17. Comparing Distributions Demo
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    18. Effects of Linear Transformations
      Standard         Video
    19. Variance Sum Law I
      Standard         Video
    20. Statistical Literacy
      Standard
    21. Exercises
      Standard

  4. Describing Bivariate Data
  5. Probability
  6. Research Design
  7. Normal Distribution
  8. Advanced Graphs
  9. Sampling Distributions
  10. Estimation
  11. Logic of Hypothesis Testing
  12. Tests of Means
  13. Power
  14. Regression
  15. Analysis of Variance
  16. Transformations
  17. Chi Square
  18. Distribution Free Tests
  19. Effect Size
  20. Case Studies
  21. Calculators
  22. Glossary
 

Variability

Author(s)

David M. Lane

Prerequisites

none
  1. Measures of Variability
  2. Variability Demo
  3. Estimating Variance Simulation

Variability refers to how much the numbers in a distribution differ from each other. The most common measures are presented in "Measures of Variability." The "variability demo" allows you to change the standard deviation of a distribution and view a graph of the changed distribution.

One of the more counter-intuitive facts in introductory statistics is that the formula for variance when computed in a population is biased when applied in a sample. The "Estimating Variance Simulation" shows concretely why this is the case.