Statistics For The Behavorial Sciences: Test 1

Front 1

Sampling Error

Back 1

The naturally occurring difference between a statistic and a parameter.

Front 2

Parameter

Back 2

A characteristic that describes a population.

Front 3

Statistic

Back 3

A characteristic that describes a sample.

Front 4

Descriptive Statistics

Back 4

Simplify the organization and presentation of data, summarize

Front 5

Inferential Statistics

Back 5

Use sample data to draw inferences about populations.

Front 6

Nominal Scale

Back 6

Consists of categories that differ only in name and are not differentiated in terms of magnitude or direction.

Front 7

Ordinal Scale

Back 7

Categories are differentiated in terms of direction, forming an ordered series.
(1st,2nd,3rd);(small,med,lg)

Front 8

Interval Scale

Back 8

Categories possibly differentiated by direction and magnitude (or distance).

Front 9

Ratio Scale

Back 9

Interval scale with absolute zero point, ratios of measurement reflect ratios of magnitude.

Front 10

Guidelines for constructing a grouped frequency distribution table:

Back 10

1) about 10 intervals
2) interval width should be a simple number (2,5,10)
3) bottom score in each interval should be a multiple of the width
4) intervals should be the same width

Front 11

Frequency distribution graph for interval or ratio scale:

Back 11

Use a histogram or polygon for interval or ratio scale.

Front 12

Frequency distribution graph for nominal or ordinal scale:

Back 12

Bar graphs are used with nominal or ordinal scales.

Front 13

A skewed distribution that tails off to the right:

Back 13

positively skewed

Front 14

A skewed distribution that tails off to the left:

Back 14

negatively skewed

Front 15

3 characteristics describe distribution:

Back 15

shape,
central tendency,
variability

Front 16

The preferred measure of central tendency with numerical scores from an interval or ratio scale

Back 16

mean

Front 17

The preferred measure of central tendency when a distribution has a few extreme scores

Back 17

median

Front 18

The preferred measure of central tendency when there are undetermined(infinite) scores

Back 18

median because it is impossible to compute the mean

Front 19

The preferred measure of central tendency for data from an ordinal scale

Back 19

median

Front 20

The preferred measure of central tendency for data on a nominal scale

Back 20

mode

Front 21

Distribution where median, mean, and mode(if only one) are equal

Back 21

symmetrical distribution

Front 22

Median best measure of central tendency

Back 22

when there are:
-extreme scores or skewed distributions
-undetermined values (unfinished test)
-open-ended distributions
-ordinal scale

Front 23

Mode best measure of central tendency

Back 23

when there are:
-nominal scales
-discrete variable (#children in family)
-describing shape

Front 24

Position of median, mode, mean in skewed distributions

Back 24

-mode always to the skewed side
-mean away from skewed side
-median in the middle

Front 25

4 basic measures of variability

Back 25

-range
-interquartile range
-variance
-standard deviation

Front 26

purpose of variability

Back 26

a method of descriptive statistics, the purpose of variability is to determine how spread out the scores are in a distribution

Front 27

equation for interquartile range

Back 27

third quartile(Q3)-first quartile(Q1)

Q3-Q1

Front 28

definitional formula for the sum of square deviations (SS)

Back 28

SS=∑(X-µ)^2

Front 29

computational formula for the sum of square deviations (SS)

Back 29

SS=∑X^2 - [(∑X)^2]/N

Front 30

variance is the mean squared deviation

equation for population variance?

Back 30

σ^2=SS/N

Front 31

variance is the mean squared deviation

equation for sample variance?

Back 31

s^2=SS/n-1

Front 32

Standard deviation is the square root of variance.

equation for population standard deviation?

Back 32

σ=√(SS/N)

Front 33

Standard deviation is the square root of variance.

equation for sample standard deviation?

Back 33

σ=√(SS/n-1)

Front 34

adding a constant value to every score in a distribution

Back 34

will change the mean by the constant but will not change standard deviation

Front 35

multiplying by a constant value to every score in a distribution

Back 35

multiplying by a constant value to every score in a distribution will multiply both the mean and the standard deviation by that constant

Front 36

Degrees of freedom for the sample variance are defined as

Back 36

d∱ = n-1

Front 37

Definition of sampling error

Back 37

Sampling error is the discrepancy, or amount of error, between a sample statistic and its corresponding population parameter.

Front 38

Definition of a sampling distribution

Back 38

A sampling distribution is a distribution of statistics obtained by selecting all the possible samples of a specific size from a population.

Front 39

sampling distribution of M is

Back 39

the distribution of sample means

Front 40

Definition of central limit theorem

Back 40

For any population with mean μ and standard deviation σ, the distribution of sample means for sample size n will have a mean of μ and a standard deviation of σ/√n and will approach a normal distribution as n approaches infinity.

Front 41

value of central limit theorem

Back 41

1.describes the distribution of sample means for ANY population
2.distribution of sample means "approaches" a normal distribution very rapidly (at n=30, distribution almost perfectly normal)

Front 42

distribution of sample means tends to be a normal distribution if one of two conditions is met

Back 42

1. population from which the samples are selected is normal
2. number of scores(n) in each sample is relatively large, 30 or more

Front 43

definition of expected value of M

Back 43

mean of the distribution of sample means is equal to the population mean(µ)

Front 44

Define standard error of M(σM)

Back 44

The standard deviation of the distribution of sample means

Front 45

what does standard error measure?

Back 45

The standard error measures the standard(expected average) amount of difference between sample mean(M) and population mean(µ).

Front 46

why is the standard error considered extremely valuable?

Back 46

it specifies precisely how well a sample mean estimates its population mean

Front 47

magnitude(size) of the standard error is determined by two factors

Back 47

1.size of the sample
2.standard deviation of the population from which sample is selected

Front 48

as the sample size increases, the error between the sample mean and the population mean

Back 48

decreases

Front 49

Define the law of large numbers

Back 49

the larger the sample size(n), the more probable it is that the sample mean will be close to the population mean

Front 50

formula for standard error(σM)

Back 50

σM=σ/√n

Front 51

increasing sample size beyond n=30

Back 51

does not produce much additional improvement in how well the sample represents the population

Front 52

formula for a z-score for sample means

Back 52

z=(M-µ)/σM

Front 53

knowing the standard error gives researchers a good indication of

Back 53

how accurately their sample data represent the populations they are studying