Stats Test 2

Front 1

central tendency

Back 1

most scores tend to fall at the center of a distribution

Front 2

characteristics of a good measure of central tendency

Back 2

- similar to a lot of scores
- near the middle of the range
- should represent each score equally

Front 3

mode (unimodal, multimodal)

Back 3

most frequent score
- useful is Dv is nominal
- uni=1 multi=more than one

Front 4

median

Back 4

- geometric center
- splits distribution in half
- for odd # of scores= (n+1)/2=location of median
- for even # of scores= (n+2)/2=location of median

Front 5

mean

Back 5

- average of all scores in a distribution

Ex/n

- best measure of central tendency
- mean takes into account each individual score
- mean is mathematical center of a set of data

Front 6

sample mean

Back 6

- best estimate of population mean
- positive deviations from the mean cancel negative deviations from the mean

Front 7

mean centering

Back 7

- subtracting the mean from each score
E(x-mean)= 0

Front 8

unskewed (normal) distribution

Back 8

- parabola

Front 9

skewed

Back 9

- if you skew a distribution, you have one prominent tail
- skewed- hump in one direction, tail in another

Front 10

positively skewed distribution

Back 10

- mean>median>mode
- hump on left side, tail on right

Front 11

negatively skewed distribution

Back 11

- mean<median<mode
- hump on right, tail on left

Front 12

2 main points of statistics

Back 12

- central tendency
- variability- measures the spread of a distribution

Front 13

4 main measures of variability

Back 13

- range
- sum of squares
- standard deviation
- variance

Front 14

range

Back 14

highest - lowest

Front 15

sum of squares

Back 15

- measures total variance; sum of square deviations from the mean

SS= E(x-mean)^2

Front 16

standard deviation

Back 16

- measures average diff bw any score and the mean; square root of variance

Front 17

variance

Back 17

- measures average variability; average of sum of squared deviations from mean

S^2= SS/n

Front 18

z score

Back 18

distance bw a raw score and the mean, in standard deviation

Front 19

positive z scores

Back 19

tell u the raw score is above the mean

Front 20

negative z scores

Back 20

tell u the raw score is below the mean

Front 21

z score of 0

Back 21

tells u the raw score is the mean

Front 22

sampling error

Back 22

diff bw population mean and sample mean
- sample mean is the best unbiased estimate of the population mean bc it is never consistenly greater than or less than the population mean
- sampling error is not bad, it is expected to happen

Front 23

degrees of freedom

Back 23

the number of scores that can vary randomly in a sample

Front 24

sampling distribution

Back 24

a collection of a statistic taken from all samples in a population of size n

Front 25

sampling distribution of the mean

Back 25

a collection of all sample means of a certain sample size taken from a population

Front 26

central limit theorem

Back 26

as n --> inifinity (or N), the sampling distribution of the mean approximates a normal distribution

Front 27

characteristics of the sampling distribution of the mean

Back 27

- mean is equal to population mean
- standard deviation is the standard error of the estimate

Front 28

confidence interval

Back 28

- a range around the sample mean where we are pretty sure a population mean lies

Front 29

standard error of the mean

Back 29

measures the average deviation of the population mean and a sample size n