Stats sampling quiz vocab

Population

Population

Complete set group of individuals/items with a common study of interest for a statistical study

Sample (Representative)

Small, manageable number of subjects that represent a larger population

Parameter

Numerical summary of a population; used to describe the entire population being studied.

Statistics

Numbers that summarize, gather, and decide data from a sample

Census

complete count of the people living in a country with their info: (job/age/gender)

Simple Random Sampling

Every member of the population has a known and equal chance of being chosen at random: (names out of hat)

Stratified Random Sampling

Divide the population into groups by shared characteristic and randomly select from each: (age, occupation, gender)

Cluster Sample

Divide population into clusters and whole cluster gets picked for sample; must be HETEROGENOUS/DISTINCT

Systematic Sampling

Select samples in numbered intervals: (every 4th person, 1st person does not count)

Convenience Sampling

Choose who/what is easiest to reach for sample

Voluntary Response Sample

Only those who want to participate for sample

Multistage Sampling

Uses multiple sampling methods subsequently; must be different to be considered multistage

Bias

Systematic tendency to affect particular outcomes over another; can only be reduced, not eliminated

Voluntary Response Bias

Participate when they want to = over/under representation; usually surveys

Undercoverage

When some participants are left out/not enough people in sample = under representation

Non-Response Bias

Chosen individuals cannot be reached, or do not want to respond; we need different perspectives = under representation

Response Bias

People LIE due to sensitive/confusing topics = under representation

Random Sampling

Best method to reduce bias

Observational Study

Observe individuals w/o manipulation/intervention

Experiment

a test under controlled conditions made to demonstrate a known truth or validate a hypothesis ; develop direct CAUSE & EFFECT relationship

Response Variable

Dependent variable ( y )

Explanatory Variable

Independent variable ( x )

Experimental unit

Person/object assigned randomly to a treatment

Factor

Variables controlled during experiments to determine their effects on response variables

Treatment

Administered to experimental units

Level

Amount/magnitude of treatment given; treatment w/ more than 1 factor applied

Treatment Group

Group receiving treatment

Control

CONSTANT experimental factor; effects do NOT change with treated participants

Control group

Group that does NOT receive any treatment

Single Blind

Participants unaware of whether they received treatments

Double Blind

Participant AND researchers do not know who received treatment

Placebo

Appears as an active treatment = 0/NO treatment and is FAKE

Placebo Effect

Effects seen in people receiving no treatment

Confounding Variable

3rd variable influences both x & y variables; effect on dependent/response/y-variable cannot be distinguished from more than 1 x variables

Lurking Variable

Unknown variable that is not controlled for; usually in independent/explanatory/x-variable & observational study

Survey

Set of ?s to gather specific data from population samples

Randomization

Randomize into specific treatments to reduce differences and favoring one experimental condition over another

Blocking

Group similar participants to reduce bias between groups

Replication

Repeating experiments w/ similar characteristics for similar results

Experimental Units (Subjects)

Performed on people, items, things; Humans = samples/subjects

Response Variable

Dependent/y-variable to be measured

Explanatory Variable (Factor)

independent/x-variable that can be controlled, manipulated, changed

Treatment

Manipulation (or experimental condition) implemented by the experimenter

Blinding

Subjects/Researchers do NOT know what treatments are assigned to which experimental unit (subjects)