Statistics Exam 1

Front 1

Statistic

Back 1

A value (usually numeric) that describes a sample
Parameter is a value describing a population

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Statistics

Back 2

Set of mathematical (statistical) procedures for organizing, summarizing and interpreting info

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Population

Back 3

The set of all individuals of interest in a particular study

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Sample

Back 4

Representative subset of a population

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Parameter

Back 5

a value describing a population
Statistic is a value (usually numeric) that describes a sample

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Variable

Back 6

Characteristic/condition that changes or has diff values for diff individuals

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Data

Back 7

Measurements/observations

Front 8

Data set

Back 8

Collection of measurements/ observations

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Datum

Back 9

A single measurement/observation. Same as raw score

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Raw score

Back 10

Same as datum - single measurement/observation.

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Descriptive statistics

Back 11

Statistical procedures used to summarize, organize & simplify data

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Inferential statistics

Back 12

Techniques that allow study of samples & make generalizations about the populations from which they were selected

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Sampling error

Back 13

The discrepancy between sample stat & corresponding population parameter
Also, margin of error

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Correlational method

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2 variables are observed to determine whether there's a relationship between them
A correlational study can't demonstrate a cause & effect relationship

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Experimental method

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One var is manipulated while another is observed and measured.

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How is cause & effect relationship established?

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By controlling all other vars to keep them from influencing results

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Independent variable

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The var manipulated by researcher

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Dependent variable

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The var observed to assess effect of treatment

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Control condition

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Either no treatment or placebo

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Experimental condition

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Sample individuals receive treatment and effects are observed and measured

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(Hypothetical) Construct

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Internal attributes/characteristics that can't be directly observed but are useful for describing/explaining behavior (ex: intelligent, level of self-esteem)

Front 22

Discrete variable

Back 22

Variable having separate and indivisible categories (ex: values displayed when dice are rolled)

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(Hypothetical) Construct

Back 23

Internal attributes/characteristics that can't be directly observed but are useful for describing/explaining behavior (ex: intelligent, level of self-esteem)

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Continuous variable

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Var has infinite no. of values between 2 observed values (ex: time, weight, height)

Front 25

Operational definition

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External behaviors are used to define a construct (ex: IQ test score used to measure intelligence)

Front 26

Discrete variable

Back 26

Variable having separate and indivisible categories (ex: values displayed when dice are rolled)

Front 27

Continuous variable

Back 27

Var has infinite no. of values between 2 observed values (ex: time, weight, height)

Front 28

(Hypothetical) Construct

Back 28

Internal attributes/characteristics that can't be directly observed but are useful for describing/explaining behavior (ex: intelligent, level of self-esteem)

Front 29

Discrete variable

Back 29

Variable having separate and indivisible categories (ex: values displayed when dice are rolled)

Front 30

Operational definition

Back 30

External behaviors are used to define a construct (ex: IQ test score used to measure intelligence)

Front 31

How is cause & effect relationship established?

Back 31

By controlling all other vars to keep them from influencing results

Front 32

Independent variable

Back 32

The var manipulated by researcher

Front 33

Dependent variable

Back 33

The var observed to assess effect of treatment

Front 34

Continuous variable

Back 34

Var has infinite no. of values between 2 observed values (ex: time, weight, height)

Front 35

Control condition

Back 35

Either no treatment or placebo

Front 36

Operational definition

Back 36

External behaviors are used to define a construct (ex: IQ test score used to measure intelligence)

Front 37

Experimental condition

Back 37

Sample individuals receive treatment and effects are observed and measured

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Real limits

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Boundaries of intervals for scores represented on a continuous number line

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Upper real limit

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Top of the interval

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Lower real limit

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Bottom of the interval

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Nominal scale

Back 41

Set of categories having diff names - no quantitative distinction between categories

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Ordinal scale

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Set of categories organized in an ordered sequence - measured in terms of size/magnitude

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Interval scale

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Ordered categories that are all intervals of exactly the same size but zero point is arbitrary and doesn't indicate zero value of var

Front 44

Interval scale

Back 44

Ordered categories that are all intervals of exactly the same size but zero point is arbitrary and doesn't indicate zero value of var

Front 45

Ratio scale

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Interval scale with absolute zero point. Ratios of numbers donreflect ratios of magnitude

Front 46

Frequency distribution

Back 46

Organized tabulation of the no. Of individuals located in ea. Category on the scale of measurement

Front 47

Ratio scale

Back 47

Interval scale with absolute zero point. Ratios of numbers donreflect ratios of magnitude

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how to find N

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Add all score frequencies (f)

Front 49

Frequency distribution

Back 49

Organized tabulation of the no. Of individuals located in ea. Category on the scale of measurement

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How to find proportion

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p = f/N

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how to find N

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Add all score frequencies (f)

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How to find proportion

Back 52

p = f/N

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Interval scale

Back 53

Ordered categories with intervals of exactly the same size & arbitrary zero point

Front 54

Ratio scale

Back 54

Interval scale with an absolute zero point. Ratios of numbers do reflect ratios of magnitude

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What does Greek symbol Sigma stand for?

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Sum

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Frequency distribution

Back 56

An organized tabulation of the no. of individuals located in each category on the measurement scale. Displayed as a table or graph

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Sum f = N

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Equation for finding N

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Proportion equation

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p = f/N

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Percentage equation

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p(100) = f/N (100)

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Equation to determine how many rows your frequent distribution table should have when scores are whole numbers

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Rows = highest score - lowest score + 1

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Histogram

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Bar graph with bars touching

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Modified histogram

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Stack of blocks

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Polygon

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Line graph anchored at both ends to x axis

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Distribution of scores

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Think of image of frequency distribution graph

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Bar graph

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Histogram without spaces between bars

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Symmetrical distribution

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Possible to draw vertical line through middle so one side is mirror image of the other side

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Skewed distribution

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Scores pile up on one side and taper off gradually at the other side

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Distribution tail

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The tapering end of a skewed distribution graph

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Positive skew

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Tail points to positive end of x-axis

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Negative skew

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Tail points to negative end of x-axis

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Central tendency

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Statistical measure to determine a single score defining center of distribution. Goal is to find the single score that's most representative of the entire group

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Mean

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Average score. Sum of scores/N

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Median

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The score that divides a distribution in half so that 50% of the individuals in a distribution have scores at or below the median

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Mode

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The score which occurs with the greatest frequency in a distribution

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In skewed distributions, the ------ is pulled toward the tail and the ------ is pulled even more toward the tail.

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Median
Mode

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Bimodal

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Distribution has 2 modes

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Multimodal

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Dstribution has more than 2 modes

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Diversity

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Diff that exist from one person to another

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Variability

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Quantitative measure of degree to which scores in a distribution are spread out or clustered together

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Range

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Dstance From largest score to smallest score in a distribution

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Interquartile range

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Ignores the extreme scores by measuring only range of the middle 50% of the distribution
Q3-Q1

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Semi-quartile range

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Half of the interquartile range
(Q3-Q1)/2

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Standard deviation

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Most commonly used & most important measure of variability

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Deviation

Back 84

Distance from mean
x-M

Front 85

Population variance

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Mean squared variance

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Variance

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Avg squared distance from mean
SS/N

Front 87

Standard deviation equation

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Square root of variance

Front 88

Sum of squares (SS)

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Sum of the squared deviation scores
SS = sum x squared - (sum x squared)/N

Front 89

Population standard deviation

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Lowercase sigma

Front 90

Biased sample statistic

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When average value of statistic consistently overestimates or underestimates the corresponding population parameter