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Stats 101 exam 1 > Stats -psych 101 exam1 > Flashcards

Stats -psych 101 exam1 Flashcards

1
Q

Statistics

A

the science of collecting, analyzing, and interpreting data

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2
Q

Statistic

A

the measure of some attribute of a sample

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3
Q

Studies

A

scientific approaches that aquire information about small or large groups

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4
Q

Case Studies

A

an anlysis of statistics of one element or a small sample of elements (one person, or small group)

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5
Q

Non-eperimental (Observational) studies

A

analyses that compare or measure similarities/differences within a group that we do not manipulate (only observe- all variables are independent)

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6
Q

Experimental studies

A

analyses that allow us to compare or measure similarities/differences btween groups that we did manipute

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7
Q

True experimental studies

A

studies comparing properties that were ramdomly assigned (random assignment)

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8
Q

Random Assignment

A

every subject in a group has equal chance of being assigned to the groups in the study

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9
Q

Single blind study

A

experiments where the elements are unaware of the groups they are in (reduces placebo effects)

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10
Q

Double blind study

A

experiments where the participants and the experimenters are unaware of the groups that they are in (reduces demand characteristics)

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11
Q

Quasi-experimental studies

A

studies where that compare properties of groups where assignment wasn’t possible (forced to have people in specific groups such as earthquakes or heart surgery)

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12
Q

Descriptive statistics

A

organizes, summarizes, and communicates a group of numerical observations (tells us information about a sample)

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13
Q

Inferential Statistics

A

Interpretations about a populations based on the analyses of a smaller set of information (makes inferences about the population based on the data of the sample)

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14
Q

Sample biasing

A

incorrectly assuming somehting about a population because of the sample that was used

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15
Q

Variables

A

general characteristics, usually quantified, that VARY and can be used to compare and describe

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16
Q

Variability

A

the fact that variables obtained often differ from one another

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17
Q

Good variability

A

individual differences -due to the participants themselves (individual or group)

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18
Q

Bad variability

A

measurement error (inability to measure something accurately) and Unreliability (variantions due to differences in responses to the same situation)

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19
Q

Construct

A

a hypothetical or theoritical entity that is being explored in research (e.g. happiness)

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20
Q

Operational definition

A

the systematic process if obtaining or measuring a construct (or variable)

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21
Q

Levels

A

the values that a construct can take on (subcategories of variables- levels of gender are male & female)

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22
Q

Validity

A

the extent to which the the test actually measures that is intended to measure

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23
Q

Construct Validity (Face)

A

is it measuring what we are interested in (and makes sense)

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24
Q

Predicitve Validity

A

does the measure predict realted behavior/measures

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25
Concurrent Validity
does it relate to another measures that are supposed to be measuring the same thing (using 2 diff tests at the same time)
26
Reliability
Consistency in measurement
27
Inter-rater reliability
degree of agreement among raters
28
Test-retest reliability
re-testing subjects to obtain the same results at diff points in time
29
Internal reliability
testing with diff tests to measure the same charactertistics (parallel forms and similar items)
30
Continuous variables
variables that can assume an infinite number of values
31
Discrete variables
variables with finite set of values (whole values)
32
Categorical (Nomial Variables)
variables that have no numerical meaning (gender, fav ice cream)
33
Quantitative variables
variables that are represented as numbers (can be continuous of discrete)
34
Ordinal
quantitative discrete variable that have a natural order, but the precise distance between values is not defined (e.g. grandes," rank" in school)
35
Interval
Variables that have values where the distance between them is meaningful and consistent but the zero is Not meaningful (IQ scores, tem in Fahrenheit)
36
Ratio
Interval variable where there is a TRUE Zero and where ratios of values make sense (distance, speed, Temp in Kelvin)
37
Independent Variable
Variables that have at least 2 levels that we either manipulate or observe in a group (usually discrete)
38
Dependent Variable
Variables that are believed to ve caused by or changed by the Indp V. (Dependent var. are only in experimetal studies)
39
Presenting Data about Variable
Freq Distrt, Grouped freq distrt, histograms, freq polygons, bar graphs
40
preseting dara about multiple variables
scatterplots, complex bar graphs, time series, line charts(complex freq polygons)
41
Histograms
can be used for variables that are quantitative (use RL)
42
Frequency Polygons
use midpoints
43
Bar graphs
used for discrete variables- bars dont touch
44
complex bar graphs
multiple variables- Primary and secondary groups
45
Time series/line charts
look like multiple freq polygons
46
Population
a complete se of people, events, or scores that we're interested in
47
Parameter
the measurable characteristic of the population that is of inteterest
48
Sample
a subset of the population
49
Statistic
the measurable characteristic of the sample of the popl that we're interested in
50
Sampling
selecting a subset of the population to collect data from (statistic) in order to make inferences about the population (parameter)
51
Convenience sampling
taking a select sample from the population that is easily accessible. This is the most common one but not the most reliable.
52
Voluntary Sampling
sampling that is obtained through willing participation of particular individuals. Problems are getting a biased sample; people that are willing to participate either have nothing to do, or are unemployed (doing it for the money). This is a non representative of the population.
53
True random sampling
taking the ENTIRE population and selecting a random sample from that population
54
Cluster sampling
random sampling of organized groups of individuals from the population. Organize the population into groups, or clusters, then choose a cluster and either sample every subject from the cluster of get a random sample from that cluster)
55
Stratified (representative sampling)
identify major characteristics of interest in the population (such as gender, age, race) and generating a sample that is proportionately equivalent to the population (e.g. if 40% of the student population are males then make sure 40% of your samples are males)
56
Pseudo-random sampling
taking everyone that is accessible from a population and selecting a random sample from that group.
57
Random assignment
means that everyone in the study has an equal chance of being in the experimental condition/levels
58
Randomized block design (Stratified assignment)
is creating equivalent groups based on important characteristics. This creates a balance in the group of study using characteristics such as gender, age or any other variable that might impact the study.
59
Convenient assignment
assignment of individuals based on the experimenters discretion (this can be very bad is its a biased convenient assignment)
60
SAMPLE STATISTICS
refers to the statistics that describe a single sample, for instance the sample mean or the sample variance (quantities that characterize samples of raw scores) Sampling statistics-
61
SAMPLING STATISTICS
(quantities that characterize sampling distribution of statistics from multiple samples. E.g. mean of means and standard error of the distributions means)- this refers to the statistics that describe the distribution of the sample statistics from multiple samples. For instance the mean or standard deviation of multiple sample means
62
Subjective probability
probability based in individual's opinion of the likelihood that an event will occur, or an event or relationship is due to more than chance (if rare occurances happen then we think of them w/high importance but it doesnt mean is true)
63
Expected probalitity
a measure of the ACTUAL probability of an outcome if the outcomes were random and repeated many times (e.g. .5 head or tails )
64
Hypothesis testing
statistically verifying that the probability of an outcome is significantly different than chance
65
Null Hypothesis
no effects, differences, or similarities on or between variables
66
Alternative Hypothesis
the oposite of the null (implies that Ho is false)
67
Type 1 error
Reject null hypothesis when its actually true (due to random chance, oversensitive tests, unethical behaviors, accidental impacts such as placebo effect, 3rd variables, unreliable measures)
68
Type 2 error
Fail to reject Ho when its actually false (due to random chance, poor unraliable measures, overly stringesnt tests, Acc imp-third var, paticipant bias)
69
Measures of variability
allow us to talk about how close from the mean the scores in the distribution are (also called measures of spread, and are calculated for quantitative data)

Stats 101 exam 1 (1 decks)

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