Chapter 4 Flashcards

1
Q

Population

A

The entire group of people you want information about in a statistical study

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2
Q

Census

A

Sampling every individual in the population

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3
Q

Sample

A

Ways to chose a sample from a population

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4
Q

Convenience sample

A

Choosing individuals from the population who are easy to reach results in

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5
Q

Bias

A

Consistently overestimate or consistently underestimate the value you want to know

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6
Q

Voluntary response sample

A

Consist of people who choose themselves by responding to a general invitation

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7
Q

Random sampling

A

Using a chance process to determine which members of a population are included in the sample

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8
Q

Simple random sample (SRS)

A

Size n is chosen in such a way that every group of n individuals in the population has an equal chance to be selected as the sample

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9
Q

Sampling badly

A

Using sampling bias

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10
Q

Stratified random sample

A

Starts by classifying the population of similar individuals, called strata. Choose a separate SRS in each stratum and combine them to form the sample

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11
Q

Strata

A

Classifying the population into groups of similar individuals

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12
Q

Cluster sample

A

Start by classifying the population into groups of individuals that are located near each other, called clusters. Choose an SRS of the clusters. All individuals in cluster are in sample

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13
Q

Cluster

A

Classifying the population into groups of individuals that are located near each other

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14
Q

Inference

A

The proves of drawing conclusions about a population on the bias of sample data

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15
Q

Undercoverage

A

Occurs when some members of the population cannot be chosen in the sample

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16
Q

Nonresponse

A

Occurs when an individual chosen for the sample can’t be contacted or refuses to participate

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17
Q

Observational study

A

Observes individuals and measures variables of interest but does not attempt to influence the responses

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18
Q

Experiment

A

Deliberately imposes some treatment on individuals to measure their respinses

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19
Q

Confounding

A

Occurs when two variables are associated in such a way that their effects on a response variable cannot be distinguished from each other

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20
Q

Treatment

A

A specific condition applied to individuals in an experiment

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21
Q

Experimental units

A

The smallest collection of individuals to which treatments are applied

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22
Q

Subjects

A

Human beings that are experimental units

23
Q

Factors

A

Explanatory variables in an experiment

24
Q

Random assignment

A

Experimental units are assumed to treatments using a chance process

25
Completely randomized design
The experimental units are assigned to the treatments completely by chance
26
Control groups
Provides a baseline for comparing the effects of other treatments
27
Control
Keep other variables that might effect the response the same for all groups
28
Replication
Using enough experimental units to distinguish a difference in the effects of treatments from chance variation due to random assignment
29
Blind
Experiment in which the subjects are unaware of which treatment they are receiving
30
Double blind
Experiment in which neither the subjects or the people who interact with them and measure their response variable know which treatment a subject has
31
Statistically significant
An observed effect so large that it would rarely occur by chanve
32
Block
A group of experimental units that are known before the experiment to be similar in some way that is expected to affect the response to the treatments
33
Randomized block design
The random assignment of experimental units to treatments is carried out separately within each block
34
Matched pairs
A type of randomized block design for comparing two treatments in which the idea is to create blocks by matching pairs of similar experimental units
35
Causation
When changes in the explanatory variable cause changes in the response variable
36
3 pillars of an experiment
1. Random 2. Replication 3. Control
37
What does replication do?
Reduces variability
38
What does control do?
Controls/eliminates lurking or confounding variables
39
Completely randomized experiment
SRS of experiments
40
Block design
Stratified experiment
41
Random sampling
HOW you choose people to be a part of a study
42
Random assignment
Occurs AFTER people are selected
43
Placebo effect
When a fake treatment works
44
What are the benefits of an experiment?
If all 3 pillars are met, then causation can be made
45
What do experiments do?
IMPOSE A TREATMENT
46
Simple random sample (SRS)
Number the population, choose the sample by random *use in experiments, helps divide groups evenly
47
Stratified
Split population into similar groups. Then sample from each group
48
Cluster
Split population into groups. Sample. Then choose ENTIRE groups of people
49
Systematic
Number the population, randomly choose a PLACE then choose every _th person
50
Multistage
Divide groups into equal areas and randomly choose areas (geographic)
51
Convenience (ex of bias)
Individuals easiest to reach
52
Voluntary (ex of bias)
People volunteer to participate by responding
53
Nonresponse
Majority of population didn’t answer