Chapter 4 Flashcards
variability
the quantitative measure of the differences between scores in a distribution
- describes the degree to which the scores are spread out or clustered together
deviation
distance from the mean
variance
the mean of the squared deviations
- the average squared distance from the mean
standard deviation
square root of the variance
-provides a measure of the standard or average distance from the mean
sum of squares
SS
-the sum of the squared deviations scores
- try to use the sum of the squared deviations scores as the definition for SS
population variance
Sigma squared
- equals mean squared distance from the mean
- is obtained by dividing the sum of squares by N
population standard deviation
symbol Sigma
- equals the square root of the population variance
sample variance
symbol S squared
- equals the mean Square distance from the mean
- obtained by dividing the sum of squares by n -1
sample standard deviation
symbol S
-the square root of the sample variance
degrees of freedom
= [sample of number of scores for the sample variance]-1
-number of scores in the sample that are independent and free to vary
unbiased
If the average value of the sample statistic is equal to the population parameter
biased
if the average value of the sample statistic either underestimate or overestimate the corresponding population parameter
what are the qualities of variance
- stability
- mathematically tractable
- senstive to extreme scores
- accurately reflects the variability in scores
- unbiase therefore we can use in inferential stats
sampling distribution
frequency distribution of a statistic for all possible random samples of size n, drawn with replacement
sampling distribution of the mean
frrequency distribution of smaple means for the sampling distubution of the mean
