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  1. Introduction
  2. Graphing Distributions

  3. Summarizing Distributions
    1. Contents
      Standard
    2. Central Tendency
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         Video
    3. What is Central Tendency
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         Video
    4. Measures of Central Tendency
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         Video
    5. Balance Scale Simulation
      Standard
         Video
    6. Absolute Differences Simulation
      Standard
    7. Squared Differences Simulation
      Standard
    8. Median and Mean
      Standard  
    9. Mean and Median Demo
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    10. Additional Measures
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         Video
    11. Comparing Measures
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         Video
    12. Variability
      Standard
         Video
    13. Measures of Variability
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         Video
    14. Variability Demo
      Standard
    15. Estimating Variance Simulation
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    16. Shapes of Distributions
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         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
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  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
 

Median and Mean

Author(s)

David M. Lane

Prerequisites

What is Central Tendency, Measures of Central Tendency

Learning Objectives
  1. State when the mean and median are the same
  2. State whether it is the mean or median that minimizes the mean absolute deviation
  3. State whether it is the mean or median that minimizes the mean squared deviation
  4. State whether it is the mean or median that is the balance point on a balance scale

In the section "What is central tendency," we saw that the center of a distribution could be defined three ways: (1) the point on which a distribution would balance, (2) the value whose average absolute deviation from all the other values is minimized, and (3) the value whose average squared difference from all the other values is minimized. From the simulation in this chapter, you discovered (we hope) that the mean is the point on which a distribution would balance, the median is the value that minimizes the sum of absolute deviations, and the mean is the value that minimizes the sum of the squared deviations.

Table 1 shows the absolute and squared deviations of the numbers 2, 3, 4, 9, and 16 from their median of 4 and their mean of 6.8. You can see that the sum of absolute deviations from the median (20) is smaller than the sum of absolute deviations from the mean (22.8). On the other hand, the sum of squared deviations from the median (174) is larger than the sum of squared deviations from the mean (134.8).

Table 1. Absolute and squared deviations from the median of 4 and the mean of 6.8.
ValueAbsolute Deviation from MedianAbsolute Deviation from MeanSquared Deviation from MedianSquared Deviation from Mean
224.8423.04
313.8114.44
402.807.84
952.2254.84
16129.214484.64
Total2022.8174134.8

Figure 1 shows that the distribution balances at the mean of 6.8 and not at the median of 4. The relative advantages and disadvantages of the mean and median are discussed in the section "Comparing Measures" later in this chapter.

Figure 1. The distribution balances at the mean of 6.8 and not at the median of 4.0.

When a distribution is symmetric, then the mean and the median are the same. Consider the following distribution: 1, 3, 4, 5, 6, 7, 9. The mean and median are both 5. The mean, median, and mode are identical in the bell-shaped normal distribution.

 

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