AP Stats 3A & 3B
Definition of Terms:
Population: Entire group of individuals we want information about.
Sample: Subset of individuals in the population from which we collect data.
Examples:
a. Quality Control in Factories:
Population: All computer monitors produced during a particular hour.
Sample: 10 monitors selected for inspection.
b. Election Survey:
Population: All registered voters.
Sample: 1000 registered voters surveyed to predict election outcome.
A sample is used to represent the population instead.
Key Definitions:
Census: Collects data from every individual in the population.
Sample: A smaller group selected for study to infer results about the population.
Observational Study: Observes individuals and measures variables of interest without influencing responses.
Retrospective: Examines existing data.
Prospective: Follows individuals into the future.
Experiment: Deliberately imposes treatments to measure responses and establish cause-and-effect relationships.
Random Sampling:
Definition: A chance process to select members of the sample.
Importance: Helps to obtain a representative sample.
Simple Random Sample (SRS):
Every group of individuals has an equal chance of being selected.
Sample without replacement, meaning once chosen, individuals cannot be selected again.
Stratified Sampling: Divides population into strata (e.g., grade levels) and samples proportionately from each.
Cluster Sampling: Involves randomly choosing entire clusters (e.g., classrooms) and including all members.
Example: Interviewing every 20th voter at polling places.
Systematic Sampling: Selects samples based on a predefined system (e.g., every 5th or 10th individual).
Convenience Sample: Involves individuals that are easy to reach.
Voluntary Response Sample: Composed of individuals who voluntarily choose to participate, often leading to bias due to self-selection.
Definition of Bias: A design that consistently results in underestimating or overestimating the value being measured.
Definition of Terms:
Population: Entire group of individuals we want information about.
Sample: Subset of individuals in the population from which we collect data.
Examples:
a. Quality Control in Factories:
Population: All computer monitors produced during a particular hour.
Sample: 10 monitors selected for inspection.
b. Election Survey:
Population: All registered voters.
Sample: 1000 registered voters surveyed to predict election outcome.
A sample is used to represent the population instead.
Key Definitions:
Census: Collects data from every individual in the population.
Sample: A smaller group selected for study to infer results about the population.
Observational Study: Observes individuals and measures variables of interest without influencing responses.
Retrospective: Examines existing data.
Prospective: Follows individuals into the future.
Experiment: Deliberately imposes treatments to measure responses and establish cause-and-effect relationships.
Random Sampling:
Definition: A chance process to select members of the sample.
Importance: Helps to obtain a representative sample.
Simple Random Sample (SRS):
Every group of individuals has an equal chance of being selected.
Sample without replacement, meaning once chosen, individuals cannot be selected again.
Stratified Sampling: Divides population into strata (e.g., grade levels) and samples proportionately from each.
Cluster Sampling: Involves randomly choosing entire clusters (e.g., classrooms) and including all members.
Example: Interviewing every 20th voter at polling places.
Systematic Sampling: Selects samples based on a predefined system (e.g., every 5th or 10th individual).
Convenience Sample: Involves individuals that are easy to reach.
Voluntary Response Sample: Composed of individuals who voluntarily choose to participate, often leading to bias due to self-selection.
Definition of Bias: A design that consistently results in underestimating or overestimating the value being measured.
