outcome 4 vocab

Question 1

population

Answer 1

the entire group of individuals about which we want info

Question 2

sample

Answer 2

population from which we are actually collecting info

Question 3

bad sampling methods

Answer 3

convenience sampling and voluntary response sampling

Question 4

simple random sample

Answer 4

n individuals chosen in such a way that every individual has an equal chance to be selected

Question 5

what to include when describing hat method

Answer 5

equal sized pieces of paper, shake the hat, without replacement

Question 6

stratified random sampling

Answer 6

classify population in groups of similar individuals (strata). Then, choose separate SRS in each stratum and combine to form full sample.
EX: divide school pop into grades

Question 7

pro of stratified random sampling

Answer 7

to get equal representation

Question 8

how to choose variable for stratifying

Answer 8

choose a variable that reduces the variability the most, or is most strongly associated with the response variable

Question 9

cluster sampling

Answer 9

Divide population into smaller groups (clusters should mimic characteristics of population), then choose SRS of clusters (all individuals included in sample)

Question 10

cons of sampling

Answer 10

• under coverage bias: some groups in pop are left out of process of choosing sample
• nonresponse bias: an individual chosen can’t be contacted/ chooses to not respond
• response bias: systematic pattern of incorrect responses in a sample survey
• bad wording of questions

Question 11

census

Answer 11

data from all individuals in population

Question 12

systematic sampling

Answer 12

sampling every nth member of the population

Question 13

pro of cluster sampling

Answer 13

easier

Question 14

observational study

Answer 14

observes individuals + measures variables, doesn’t attempt to influence responses

Question 15

experiment

Answer 15

deliberately imposes some treatment on individuals to measure their responses

Question 16

confounding variable

Answer 16

when 2 variables are associated in such a way that their effects on a response variable cannot be distinguished from one another

Question 17

treatment

Answer 17

a specific condition applied to the individuals in an experiment

Question 18

experimental units

Answer 18

who’s receiving the treatment? smallest collection of individuals to which treatments are applied

Question 19

keys to a good experimental design (4)

Answer 19

• comparison
• random assignment
• control
• replication

Question 20

Completely randomized design

Answer 20

the experimental units are assigned to treatments completely by chance (hat method)

Question 21

Randomized block design

Answer 21

the random assignment of experimental units to treatments is carried out separately within each block

Question 22

block

Answer 22

group of experimental units that are known before to be similar in some way that will affect the response of the treatments

Question 23

Matched pairs design

Answer 23

a randomized blocked experiment where each block consists of a matching pair of similar experimental units

Question 24

double blind experiment

Answer 24

neither the subjects or those interacting with them and the person measuring the response variable know which treatments a subject recieved

Question 25

when can we generalize an experiment to the whole population

Answer 25

if there is random assignment

Question 26

when can we assume a cause and effect relationship from an experiment

Answer 26

if there is random selection