Chapter 4 Stats

Question 1

Population:

Answer 1

Wider group you study, often described as the set. Ex: IKEA survivors

Question 2

Units

Answer 2

Each individual piece of the population. EX: Jared, Meatball Guy

Question 3

Census

Answer 3

Collecting data on the entire sample

Question 4

Bias:

Answer 4

Describes a method with the tendency to over/underrepresent population amounts.

Question 5

Nonrepresentative:

Answer 5

Describes a sample that falsely represents the population. Harder to test, but biased tests are more likely to be nonrepresentative.

Question 6

Sample selection bias / Sampling Bias:

Answer 6

Systematically too high or low sampling methods. General terms.

Question 7

Size Bias:

Answer 7

Vague. Frequency bias, or stuff depends on physical size.

Question 8

Sampling Frame

Answer 8

Listing all possible units in a population.

Question 9

Voluntary Response Bias:

Answer 9

Relying on voluntary responses. Encourages passionate answers.

Question 10

Convenient Sampling:

Answer 10

Introduces a conveience bias, where samples aren’t varied.

Question 11

Judgement Sample:

Answer 11

Relying on an expert’s judgement to narrow down the sample. Non-probabilistic. Often quicker and more useful at the cost of experimental bias.

Question 12

Non-Response Bias:

Answer 12

Significant portion of sample was non-responsive, effectively culling sample size.

Question 13

Questionnaire Bias:

Answer 13

Like a push poll - “are you into the suffering of children or are you voting for George Washington?”

Question 14

Incorrect Response Bias:

Answer 14

Lying, bad memory, possibly from a result of peer pressure.

Question 15

Simple Random Sample (SRS):

Answer 15

All possible samples of a given fixed size are equally likely. Ex: Random number generator.

Question 16

Stratified Random Sample (Strata)

Answer 16

Dividing units into non-overlapping subgroups, and taking a few from each. Few from all, essentially. Useful for making sure certain groups are represented.

Question 17

Cluster Sample:

Answer 17

Dividing units into non-overlapping clusters, then selecting a few clusters and looking at every data point. All from Few.

Question 18

Two Stage Sampling:

Answer 18

Cluster, then Strata. Dividing units into non-overlapping clusters, randomly taking a few clusters, and randomly looking at a few data points there.

Question 19

Systemic Sample with Random Start

Answer 19

Counting off samples into non-overlapping groups, then choosing one group. Like every 8th kid in a class after starting at some random sample. Better when population size is uncertain.

Question 20

Lurking Variable:

Answer 20

Secret third option. Obfuscated variable creating a correlation between two variables.

Question 21

Treatment

Answer 21

Compares conditions in experimental testing. Two or more.

Question 22

Response

Answer 22

Outcome variable in experimental testing.

Question 23

Confounding:

Answer 23

Mixing together outcomes so effects cannot be separated, oft-accidentally. Less likely with randomizing.

Question 24

Observation Study:

Answer 24

No treatment assigned by experimenter. Considered less reliable against confounding, as more factors may naturally confound. Factors for conditions (say, thymus size) into levels (small or big)

Question 24

Clinical Trial:

Answer 24

Randomized & controlled experiment, often deploying double blind (researcher & patient) unawareness on placebo sample.

Question 25

Experimental Units:

Answer 25

Groups (people, animals, families, classrooms) a treatment is assigned to. Analogous to sample size. Ex. Getting 700 cell samples for two dif sponges, meaning a sample of one for the purposes of comparison.

Question 26

Replication:

Answer 26

Repeating treatment on different units.

Question 27

Balanced:

Answer 27

Treatments have same number of units assigned

Question 28

Blocking:

Answer 28

Grouping similar units in a sample and taking levels of that blocked variable to minimize confounding. Similiar to controlled variables.