PSYC 2530 Lecture Notes  Central Tendency, Statistical Model, Standard Deviation
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Published on 1 Feb 2013
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Beyond typical value > typical dispersion

Intuition: spread, range, spread, "more or less"

Decrease in predictability
Decrease in homogeneity
Increase in Heterogeneity
Increase in variability =
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Problem:

Goal : measure spread of scores in a distribution

Descriptive : degree to which scores in a sample are spread out or clustered

Small V = scores clustered > any score gives a good representation of the population
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Large V = scores widely spread > a few extreme scores can give a distorted picture of the general
population
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Inferential : how accurately any one score or sample represents the population

Variability
Range
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Standard deviation & variance
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Variability can be measured by the

Between scores, variability measures distance

Measuring Variability
Total distance spanned by sample scores

Only reflects extremes (typical = middle)
"rough" (approximation, unrefined)

Range
Ch. 4  Variability
Wednesday, September 19, 2012
3:06 PM
Lecture Notes Page 1
Only reflects extremes (typical = middle)
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Unstable
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Fast and easy to calculate

Value (e.g., winter temperature)

e.g., 7 + 2, "more or less"
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"Normal", "common", "typical", dispersion

Try average distance between scores & mean

Standard Deviation
Sum deviations for each score (value  mean)

Calculate "average" distance
Sum of Squares (SS): good measure of overall variability, but dependent on the number of scores

So, we calculate the average variability: SS/(n1)

This value is called the variance (s2)

Variance
Sample variance : s2

Population variance : σ2

Standard Deviation
Standard Deviation: calculation
Sum of squared deviations score to mean?
Note: N1, not N
Lecture Notes Page 2