Re Ii Stats

Front 1

frequency

Back 1

number of participants or cases; N=a population, n=a sample

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proportion

Back 2

part of 1

Front 3

frequency distribution/polygon

Back 3

a table/drawing that shows how many participants have each score

Front 4

positive skew

Back 4

distribution curve with longer tail to the right

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negative skew

Back 5

distribution curve with longer tail to the left

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bimodal distribution

Back 6

has two high points, most likely to emerge when human intervention or a rare event has changed the composition of a population

Front 7

mean

Back 7

balance point in a distribution of scores, or, the point around which all the deviations sum to zero

Front 8

computation of mean

Back 8

sum scores and divide by number of scores, symbolized by M, or m, or (x-bar)

Front 9

notes on mean

Back 9

pulled in direction of extreme scores therefore inappropriate for skewed distributions; should only be used with interval and ratio scales

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median

Back 10

middle point of a distribution, 50% of cases above and 50% below it.

Front 11

notes on the median

Back 11

insensitive to extreme scores therefore good for skewed distributions; cannot be used on nominal data

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mode

Back 12

most frequently occurring score; can be used for nominal data, though percentages may be more informative

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variability

Back 13

differences among scores; also called spread or dispersion

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range

Back 14

difference between highest and lowest score (but being based on the two extreme scores can be a weakness)

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outliers

Back 15

scores that lie far outside the range of most other scores

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interquartile range

Back 16

range of the middle 50% of the participants (ignores outliers)

Front 17

standard deviation

Back 17

most frequently used measure of variability; provides an overall measurement of how much scores differ from the mean

Front 18

standard dev. and normal curve

Back 18

about 68% or two-thirds of cases will lie within one standard deviation of the mean

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correlation

Back 19

extent to which two variables are related

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census

Back 20

study in which all members of a population are included

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direct relationship is also called

Back 21

positive relationship (high or low on both variables)

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inverse relationship is also called

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negative relationship (high on one variable, low on the other)

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correlation is not

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causation

Front 24

what is needed to study cause and effect?

Back 24

a controlled experiment

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Pearson correlation coefficient (Pearson r, Pearson product-moment correlation coefficient)

Back 25

describes relationship between 2 variables: -1 (perfect inverse) to 1 (perfect direct relationship); 0 is absence of relationship (this is NOT a proportion)

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coefficient of determination (r squared)

Back 26

when converted to a percentage (by multiplying by 100) tells us how effective one variable is in predicting another

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null hypothesis

Back 27

for the difference between 2 sample means, the true difference between the means is zero; or, there is no true difference between the means

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research hypothesis

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a researchers "expectation" or personal hypothesis

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directional hypothesis

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that one group's average will be higher than another's

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nondirectional hypothesis

Back 30

that one group's average will be higher but there's insufficient info to say which

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significance test (p-italicized)

Back 31

tests the null hypothesis and yields a probability that it is true

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what kinds of tests are used to test the null hypothesis?

Back 32

inferential tests

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probability level

Back 33

(also called alpha level) the level for rejecting the null hypothesis, that is, that it is not true; commonly .05

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Type I error

Back 34

the error of rejecting the null hypothesis when it is correct

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synonym for "rejecting the null hypothesis"

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results are statistically significant

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Type II error

Back 36

error of failing to reject the null hypothesis when it is false

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t-test

Back 37

tests the difference between two sample means to determine statistical significance and yields a probability that the null hypothesis is correct

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3 factors resulting in a low probability of a correct null hypothesis (ie, that it will be rejected)

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1) larger samples (reduces sampling error)
2) larger difference between means (random sampling produces few large differences)
3) smaller variance (less sampling error)

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chi-square

Back 39

tests for differences among frequencies, ie, for nominal data where number of cases and percentages are reported (can't compute mean or standard deviation for nominal data)

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is significant difference (ie, reliable) the same as a large difference?

Back 40

no

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limitations of significance testing

Back 41

1) fails to indicate size of difference
2) does not assess the practical significance of a difference

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effect size

Back 42

standardizes the size of the difference between two means

Front 43

Cohen's d

Back 43

measure of effect size, in standard deviation units (there are only about 3 standard deviation units above and below the mean).