Title: Measures of Central Tendency
1Measures of Central Tendency
2Measures 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
3Measures of Central Tendency
- Mode most frequently occurring data point
4Measures of Central Tendency
Data Point Frequency
0 2
1 5
2 7
3 14
4 15
5 8
6 5
5Measures 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
6Measures 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
7Measures 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
8Measures 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?
9Measures 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
10Measures 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
11Measures 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)
12Measures of Central Tendency
13Measures 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
14Measures 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
15Measures 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