# 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
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Intuition: spread, range, spread, "more or less"
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Decrease in predictability
Decrease in homogeneity
Increase in Heterogeneity
Increase in variability =
Problem:
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Goal : measure spread of scores in a distribution
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Descriptive : degree to which scores in a sample are spread out or clustered
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Small V = scores clustered --> any score gives a good representation of the population
Large V = scores widely spread --> a few extreme scores can give a distorted picture of the general
population
Inferential : how accurately any one score or sample represents the population
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Variability
Range
Standard deviation & variance
Variability can be measured by the
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Between scores, variability measures distance
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Measuring Variability
Total distance spanned by sample scores
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Only reflects extremes (typical = middle)
"rough" (approximation, unrefined)
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Range
Ch. 4 - Variability
Wednesday, September 19, 2012
3:06 PM
Lecture Notes Page 1
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Only reflects extremes (typical = middle)
Unstable
Fast and easy to calculate
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Value (e.g., winter temperature)
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e.g., 7 +- 2, "more or less"
"Normal", "common", "typical", dispersion
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Try average distance between scores & mean
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Standard Deviation
Sum deviations for each score (value -- mean)
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Calculate "average" distance
Sum of Squares (SS): good measure of overall variability, but dependent on the number of scores
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So, we calculate the average variability: SS/(n-1)
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This value is called the variance (s2)
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Variance
Sample variance : s2
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Population variance : σ2
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Standard Deviation
Standard Deviation: calculation
Sum of squared deviations score to mean?
Note: N-1, not N
Lecture Notes Page 2
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