Lecture 5

Question 1

What is Variability?

Answer 1

The scatter of scores in a distribution that describes the spread of data
- deviance/variance
- standard deviation & coefficient of variance

Question 2

What is Deviance (d)?

Answer 2

The distance of each raw score from the mean

Question 3

What is total deviance?

Answer 3

The sum of all deviances = 0
*in any deviance, the sum of all deviances is always 0

Question 4

What is the Sum of Squares (SS)?

Answer 4

Sum of the squared deviations (adding up all the squared deviations)
- value depends on # of scores in the data
- must square the deviations so that they don’t cancel out… this gives us the magnitude of the deviation

Question 5

What is Variance (V or s^2)?

Answer 5

Average distance between the mean & the data point
- when working with data from a sample, we refer to the facts as statistics or estimates but when working with population data, we call the facts parameters

Question 6

What is Standard Deviation (SD)?

Answer 6

The square root of the variance
- used to summarize variability
- easiest measure of variability to interpret
- reflects deviation from the mean

Question 7

What is something to note when calculating standard deviation of a sample?

Answer 7

When calculating SD of a sample, a correction factor must be applied to the equation so that the estimate of the population is not biased by a small sample
* use N-1 as the denominator instead of N (N-1 is a degree of freedom term)

Question 8

What does the Coefficient of Variation allow for?

Answer 8

Coefficient of variation allows us to compare the 2 standard deviations
- example; we wanted to compare the standard deviations from 2 different variables; 1RM squat vs vertical jump (squat in kg & jump in cm)