Module 0-1

Question 1

observational study

Answer 1

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

Question 2

Can observational studies demonstrate casual inferences?

Answer 2

no

Question 3

random assignment

Answer 3

randomly assigning individuals into treatment groups

Question 4

random sampling

Answer 4

randomly selecting individuals from a population

Question 5

population inference

Answer 5

ability to make a generalization about the population based on results

Question 6

casual inference

Answer 6

draws a conclusion based on a clinical trial/observational data or ability to see the effects different causes will have

Question 7

what conclusions are drawn from random assignments?

Answer 7

casual inferences

Question 8

what conclusions are drawn from random sampling?

Answer 8

population inferences

Question 9

retrospective study

Answer 9

observational study where subjects are selected and then previous conditions/behaviours are studies/determined

Question 10

what is a census?

Answer 10

a sample that includes everyone and samples the entire population

Question 11

we should only make population inferences when we have what?

Answer 11

random sampling

Question 12

simple random samples (SRS)

Answer 12

taking a sample from the population where there is an equally chance of being chosen, this is drawn at random

Question 13

stratified random sampling

Answer 13

population in divided into alike groups called strata and then the SRS is taken from each strata

Question 14

systematic random sampling

Answer 14

start at a random individual then sample every nth person - there is a system/pattern

Question 15

cluster random sampling

Answer 15

putting the population into similar groups called clusters, then selecting one or a few clusters at random and then sampling everyone in the chosen clusters

Question 16

a tendency for a sample to differ from the population in some systematic way is considered what?

Answer 16

bias

Question 17

selection bias (undercoverage)

Answer 17

some portion of the population isn’t sampled at all or is has a smaller representation
ie. survey of households will miss people who don’t have a fixed address or people in prison

Question 18

response bias

Answer 18

something in the survey design that influences responses
ie. if asked about something illegal

Question 19

voluntary bias

Answer 19

when individuals can chose if they want to participate or not
ie. internet polls

Question 20

nonresponse bias

Answer 20

when part of those sampled fail to respond
ie. telephone survey, some may be on vacation

Question 21

we should only make causal inferences when we have what?

Answer 21

random allocation

Question 22

lurking variables

Answer 22

related to both groups and the response
ie. testing dogs on new food and cats on safe food - wouldn’t tell any differences

Question 23

2 main types of study designs ?

Answer 23

1. observational study
2. randomized experiment

Question 24

experiment

Answer 24

study where researcher applies different treatments to different subjects and observe the outcomes
ie. controlled clinical trial

Question 25

prospective study

Answer 25

observation study where the subjects are are followed for future outcomes

Question 26

what can have both population and causal inferences?

Answer 26

an experiment

Question 27

sampling frame

Answer 27

list of subjects in the population where sample is taken

Question 28

convenience sample

Answer 28

select individuals who are conveniently available

Question 29

voluntary response sample

Answer 29

collect data from individuals who volunteer to answer

Question 30

control group

Answer 30

experimental group that receives no treatment

Question 31

placebo

Answer 31

identical to a treatment but has no effect

Question 32

single blind

Answer 32

subjects are unaware of the treatment they are receiving

Question 33

double blind

Answer 33

both subjects and investigators are unaware

Question 34

design

Answer 34

overall plan for conducting an experiment

Question 35

factor

Answer 35

variables that experimenters control

Question 36

what are the 4 principles of experimental design

Answer 36

1. direct control - fix factors at a constant level 2. randomization - random assignment 3. replication - repeting treatment enough for better results 4. blocking - arrange groups by factors and apply treatments inside each block

Question 37

3 main aspects of statistics

Answer 37

1. design 2. description 3. inference

Question 38

design

Answer 38

"think" - planning how to obtain data

Question 39

description

Answer 39

"show" - summarizing the collected data

Question 40

inference

Answer 40

"tell" - making decisions/predictions based on data

Question 41

population

Answer 41

all elements whose characteristic are being studies ie. people in canada

Question 42

sample

Answer 42

portion of the population selected for study ie. people in alberta

Question 43

a parameter is a summary measure calculated from what kind of data?

Answer 43

population data

Question 44

a statistic is a summary measure calculated from what kind of data?

Answer 44

sample data

Question 45

what cannot assume a numerical value but is classifiable onto 2 or more categories?

Answer 45

qualitative (categorical) data

Question 46

what is measured numerically?

Answer 46

quantitative (numerical) data

Question 47

what is an example of qualitative and quantitative data?

Answer 47

qualitative: letter grade, gender, eye/hair colour quantitative: shoe size, weight, distance, height

Question 48

what kind of data only contain certain values with no intermediate values ?

Answer 48

discrete data

Question 49

what kind of data can be any numerical value?

Answer 49

continuous data

Question 50

what is an example of discrete and continuous data?

Answer 50

discrete: grade value and shoe size continuous: temperature and distance