Stats - Collecting Data

Question 1

Convenience Sampling

Answer 1

Sampling in convenient places leaving you with not very accurate results
- ex. sampling about cafeteria food on the cafeteria line - the people on the line would give it a good rating

Question 2

Bias

Answer 2

Systematically favoring a different outcome
- Sampling not randomly
- Choosing the people you sample which can lead to a different outcome because you can be choosing people that you think will give you a certain answer

Question 3

Voluntary Response Sampling

Answer 3

Allows people to choose to be in the sample by responding to a general information
- Doesn’t require you to answer and it takes time so most people wont respond
- People who respond either really love it or really hate it - strongly opinionated people (usually negative responses)
- Someone who didn’t get their way
- Response bias

Question 4

Random Sampling

Answer 4

Involving using a chance process to determine which members of a population are included in the sample
- Chosen with no bias/influence by the holder

Question 5

Simple Random Sample (SRS)

Answer 5

Size of n is chosen in such a way that every group of n individuals in the population has an equal chance to be selected in the sample
N = population
n = Sample size (>1)
- Multiply to get the amount of combinations

Question 6

Permutation

Answer 6

Used to see how many ways you are able to arrange something
-ex. 4 books on a shelf (4x3x2x1)

Question 7

Census

Answer 7

Collects data from every individual in the population

Question 8

Cluster Sampling

Answer 8

Selects a sample by randomly choosing clusters

Question 9

Strata

Answer 9

Groups of individuals in a population who share characteristics thought to be associated with the variables being measured in the study

Question 10

Systematic Sampling

Answer 10

Selects a sample from an ordered arrangement of the population by randomly selecting one of the first k individuals and choosing every kth individual there after

Question 11

Random

Answer 11

everyone has an equal chance

Question 12

Under coverage

Answer 12

When some members of the population are less likely to e chosen or cannot be chosen in a sample
- ex. having a survey on a Monday at 12 pm - excludes everyone at school or work

Question 13

Nonresponse

Answer 13

When an individual chosen for the sample can’t be contacted or refuses to participate
- may not answer - makes it not random anymore
- gives you less accurate results
- leads to response bias

Question 14

Response Bias

Answer 14

When there is a systematic pattern of inaccurate answers to a survey question
- someone obviously favoring a side
- agreeing just to be left alone
- leads to inaccurate results
- may not be intentional - they may not know the answer at the top of their head
- Someone may be messing around on the survey

Question 15

Observational Study

Answer 15

Observes individuals and measures variables of interest but does not influence the response
- Data that is already collected to see the results (research)
- Not doing anything to affect the outcome or manipulate

Question 16

Response Variable

Answer 16

Measures an outcome of a study
- The change
- Similar to an dependent variable

Question 17

Explanatory Variable

Answer 17

May help or predict changes in a response variable
-What you think will cause the change
- Similar to an Independant variable

Question 18

Confounding Variable

Answer 18

Occurs when two variables are associated in such a way that their effects on a response variable cannot be distinguished from each other
- Also known as the lurking variable
- A variable that could possibly effect the outcome

Question 19

Placebo

Answer 19

A treatment that doesn’t have any active ingredient, but is otherwise like other treatments
- A dummy treatment
- Control variable

Question 20

Treatment

Answer 20

A specific conditions applied to the individuals in an experiment

Question 21

Experimental Unit

Answer 21

Object to which a treatment is randomly assigned

Question 22

Subjects

Answer 22

Often a name for experimental units that are human beings

Question 23

Control Group

Answer 23

Control the confounding variables used to provide a baseline for comparing the effects of other treatments

Question 24

Double-Blind

Answer 24

Neither the subjects nor those who interact with them and measure the response variable know which treatment a subject is receiving

Question 25

Single-Blind

Answer 25

Either the subjects or the people who interact with them and measure the response variable don’t know which treatment a subject is receiving

Question 26

Random Assignment

Answer 26

Means that experimental units are assigned to treatments using a change method
- Avoids bias (eliminates) - the person running it doesn’t choose
- Evens out the playing field

Question 27

Control

Answer 27

Keeping other variables constant for all experimental groups
- Everything the same other than the treatment

Question 28

Replication

Answer 28

Giving each treatment to enough experimental units so that a difference in the effects of the treatments can be distinguished
- Makes it more accurate
- Not by chance but by the treatment

Question 29

Variation

Answer 29

Different sample from same population, you would get similar results but never the same
- More differences = larger sample sizes
- The larger your sample size the more accurate your results

Question 30

Block

Answer 30

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

Question 31

Randomized block design

Answer 31

The random assignment of experimental units to treatments is carried out separately within each block
- Not completely random anymore

Question 32

Matched Pairs Design

Answer 32

A common type of randomized block design for comparing two treatments
- Giving each subject both treatments

Question 33

Sampling Variablity

Answer 33

Refers to the fact that different random samples of the same size from the same population produce different estimates

Question 34

Sampling Distribution

Answer 34

Collection of all possible outcomes and averaging them

Question 35

Statistically Significant

Answer 35

When the observed results of a study are too unusual to be explained by chance alone