Psychological Statistics Chapter 4, Exams of Statistics

Psychological Statistics Chapter 4

Typology: Exams

2023/2024

Available from 04/01/2024

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Psychological Statistics Chapter 4
variability -
provides a quantitative measure of the differences between scores in a distribution and
describes the degree to which the scores are spread out or clustered together
range -
Xmax-Xmin
URL for Xmax - LRL for Xmin
deviation or deviation score -
the difference between a score and the mean
X-mu
variance -
equals the mean of the squared deviations
standard deviation -
the square root of the variance and provides a measure of the standard or average distance
from the mean
sum of squares -
the sum of the squared deviated scores
population variance -
represented by lower case sigma squared and equals the mean squared distance from the
mean.
population standard deviation -
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Psychological Statistics Chapter 4

variability - provides a quantitative measure of the differences between scores in a distribution and describes the degree to which the scores are spread out or clustered together range - Xmax-Xmin URL for Xmax - LRL for Xmin deviation or deviation score - the difference between a score and the mean X-mu variance - equals the mean of the squared deviations standard deviation - the square root of the variance and provides a measure of the standard or average distance from the mean sum of squares - the sum of the squared deviated scores population variance - represented by lower case sigma squared and equals the mean squared distance from the mean. population standard deviation -

represented by lower case sigma and equals the square root of the population variance. sample variance - represented by the symbol s squared and equals the mean squared distance from the mean (dividing the sum of squares by n-1) sample standard deviation - represented by the symbol s and equals the square root of the sample variance degrees of freedom - n-1, determines the number of scores in the sample that are independent and free to vary biased statistic - if the average value of the statistic either underestimates or overestimates the corresponding population parameter unbiased statistic - if the average value of the statistic is equal to the population parameter