Module 0

Question 1

population parameter

Answer 1

a number that describes something about an entire group or population
ex: census is done to find the population

Question 2

Census

Answer 2

A sample that includes the entire population

Question 3

Problems with taking a census

Answer 3

expensive
undercoverage (doesn’t include everyone)
time-consuming

Question 4

sample statistics

Answer 4

collecting data from a sample to provide a statistic in order to avoid the problems of taking a census

Question 5

Population inference

Answer 5

results from the sample can be generalized to the entire population

Question 6

Causal inference

Answer 6

different responses caused by treating two groups differently

Question 7

random sampling

Answer 7

selecting individuals randomly for a sample.
Can then make population inferences
ensures sample on average looks the same as the population

Question 8

extraneous factors

Answer 8

variables that are not being tested but could affect the final data to be inaccurate if not controlled

Question 9

bias

Answer 9

the tendency for a sample to differ from the corresponding population in some systematic way

Question 10

SRS

Answer 10

simple random samples
A sample chosen randomly, each with equal probability of being selected
ex: drawing names at random from a box

Question 11

sampling variability

Answer 11

different samples of the population will have slightly different results. These differences are called sampling variability.

Question 12

stratified random sampling

Answer 12

an SRS is taken from each strata of a population and the results are combined.

Question 13

strata

Answer 13

homogeneous groups that make up a population

Question 14

purpose of stratified random sampling

Answer 14

reduce bias and reduce variability

Question 15

systematic random sampling

Answer 15

start from a randomly selected individual and sample every kth person
should be no reason for the order of the list that could alter the results
less expensive

Question 16

cluster random sampling

Answer 16

splitting the population into similar groups (clusters), selecting one or a few, and perform a census on each chosen cluster
if each cluster fairly represents the population, gives an unbiased sample

Question 17

selection bias (undercoverage)

Answer 17

when a portion of the population is not sampled or has a smaller representation than in the population

Question 18

response bias

Answer 18

the survey design influences responses to give a biased result

Question 19

voluntary response bias

Answer 19

people can choose if they want to participate in the sample

Question 20

nonresponse bias

Answer 20

when a large proportion of the sample fail to respond

Question 21

random allocation

Answer 21

randomly selecting individuals for each group to reduce lurking variables

Question 22

lurking variables

Answer 22

variables related to group membership and the response. in causal inference, groups should be selected using random allocation so lurking variables do not effect the response.

Question 23

observational study

Answer 23

the investigator observes individuals but does not attempt to influence responses

Question 24

retrospective study

Answer 24

choses sample based on if individuals meet the criteria for what study they are conducting. Ex: only choses participants near powerplant

Question 25

prospective study

Answer 25

choses subjects for the sample first and then observes outcomes

Question 26

randomized, comparative experiments

Answer 26

can prove cause and effect relationship
randomly selects sample
randomly assigns sample to treatment groups
treats 2 groups differently