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Measures of Central Tendency

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Measures of Central Tendency Measures of Central Tendency Central Tendency = values that summarize/ represent the majority of scores in a distribution Three main ... – PowerPoint PPT presentation

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Title: Measures of Central Tendency


1
Measures of Central Tendency
2
Measures of Central Tendency
  • Central Tendency values that summarize/
    represent the majority of scores in a
    distribution
  • Three main measures of central tendency
  • Mean ( Sample Mean µ Population Mean)
  • Median
  • Mode

3
Measures of Central Tendency
  • Mode most frequently occurring data point

4
Measures of Central Tendency
  • Mode (34)/2 3.5

Data Point Frequency
0 2
1 5
2 7
3 14
4 15
5 8
6 5
5
Measures of Central Tendency
  • Median the middle number when data are arranged
    in numerical order
  • Data 3 5 1
  • Step 1 Arrange in numerical order
  • 1 3 5
  • Step 2 Pick the middle number (3)
  • Data 3 5 7 11 14 15
  • Median (711)/2 9

6
Measures of Central Tendency
  • Median
  • Median Location (N 1)/2 (56 1)/2 28.5
  • Median (34)/2 3.5

Data Point Frequency
0 2
1 5
2 7
3 14
4 15
5 8
6 5
7
Measures of Central Tendency
  • Mean Average ?X/N
  • ?X 191 Mean 191/56 3.41

Data Point Frequency X
0 2 0
1 5 5
2 7 14
3 14 42
4 15 60
5 8 40
6 5 30
8
Measures of Central Tendency
  • Occasionally we may need to add or subtract,
    multiply or divide, a certain fixed number
    (constant) to all values in our dataset
  • i.e. this is essentially what is done when
    curving a test
  • What do you think would happen to the average
    score if 4 points were added to each score?
  • What would happen if each score was doubled?

9
Measures of Central Tendency
  • Characteristics of the Mean
  • Adding or subtracting a constant from each score
    also adds or subtracts the same number from the
    mean
  • i.e. adding 10 to all scores in a sample will
    increase the mean of these scores by 10
  • ?X 751 Mean 751/56 13.41

Data Point 10 Frequency X
0 10 2 20
1 11 5 55
2 12 7 84
3 13 14 182
4 14 15 210
5 15 8 120
6 16 5 80
10
Measures of Central Tendency
  • Characteristics of the Mean
  • Multiplying or dividing a constant from each
    score has similar effects upon the mean
  • i.e. multiplying each score in a sample by 10
    will increase the mean by 10x
  • ?X 1910 Mean 1910/56 34.1

Data Point x10 Frequency X
0 0 2 0
1 10 5 50
2 20 7 140
3 30 14 420
4 40 15 600
5 50 8 400
6 60 5 300
11
Measures of Central Tendency
  • Advantages and Disadvantages of the Measures
  • Mode
  • Typically a number that actually occurs in
    dataset
  • Has highest probability of occurrence
  • Applicable to Nominal, as well as Ordinal,
    Interval and Ratio Scales
  • Unaffected by extreme scores
  • But not representative if multimodal with peaks
    far apart (see next slide)

12
Measures of Central Tendency
  • Mode

13
Measures of Central Tendency
  • Advantages and Disadvantages of the Measures
  • Median
  • Also unaffected by extreme scores
  • Data 5 8 11 Median 8
  • Data 5 8 5 million Median 8
  • Usually its value actually occurs in the data
  • But cannot be entered into equations, because
    there is no equation that defines it
  • And not as stable from sample to sample, because
    dependent upon the number of scores in the sample

14
Measures of Central Tendency
  • Advantages and Disadvantages of the Measures
  • Mean
  • Defined algebraically
  • Stable from sample to sample
  • But usually does not actually occur in the data
  • And heavily influenced by outliers
  • Data 5 8 11 Mean 8
  • Data 5 8 5 million Mean 1,666,671

15
Measures of Central Tendency
  • Advantages and Disadvantages of the Measures
  • Mean
  • Often you will see sums quoted instead of average
    or mean values, you should be wary of these
    statistics because they are easily skewed
  • i.e. Statistics for the performance of a
    basketball player are quoted in the newspaper, it
    says that he has 134 points over the course of
    the season, whereas other players average well
    over 200.
  • From this you would conclude that he is a
    mediocre player at best, however, it is possible
    that he has played fewer games than other players
    (due to injury)
  • Looking at averages, the player actually averages
    50 pts. per game, but has only played three
    games, whereas other players average 20 or less
    pts. over more games
  • Using this much richer information, our
    conclusions would be completely different
    AVERAGES ARE ALWAYS MORE INFORMATIVE THAN SIMPLE
    SUMS
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