Algebra 2 Statistics Flashcards

Statistics Overview

• Involves collection, analysis, interpretation, presentation, and organization of data.

Population

• Refers to all data relevant to a study, such as measurements or responses.
• Examples include census data or all students in a specific school.

Sample

• A subset of a population, which can be obtained through various methods to ensure randomness.

Random Sample

• A sample representative of the entire population, facilitating accurate conclusions.
• Example: A mixture of students from different grades in a high school.

Census

• Gathers data from an entire population but is often impractical due to time and cost involved.

Bias

• Introduces error in sampling methods, leading to over- or under-representation of a population segment.
• Example: Sampling only 10th and 11th graders in a survey.

Parameter

• Represents a numerical characteristic of a population.
• Example: The average minimum wage of teenagers in a town.

Statistic

• A numerical description derived from a sample, reflecting a characteristic of that sample.
• Example: Median salary of statisticians in a survey.

Hypothesis

• A proposed claim regarding a population characteristic, requiring analysis to distinguish significant results from random chance.

Population Proportion

• The ratio of individuals with a specific characteristic in a population compared to the total population size.

Sample Proportion

• Similar to population proportion but pertains specifically to a sample of the population.

Random Sample (Definition Reiteration)

• Ensures each member has an equal chance of selection, representing the population accurately.

Self-selected Sample

• Composed of individuals who voluntarily participate, such as an online survey.

Systematic Sample

• Involves selecting every Kth member from an ordered population.
• Example: Picking every 6th student entering a classroom.

Convenience Sample

• Involves members who are easily accessible.
• Example: Surveying students within a specific class.

Stratified Sample

• Division of a population into groups sharing similar traits, with samples drawn randomly from each group.
• Example: Sampling 50 students each from freshmen, sophomores, juniors, and seniors.

Cluster Sample

• Involves dividing the population into clusters and selecting all members from specific clusters.
• Example: Selecting all students from several chosen homerooms.

Unbiased Sample

• Accurately represents the population, ensuring fair and accurate analysis.

Biased Sample

• Over- or under-represents aspects of the population, leading to skewed results.

Experiment

• Imposes a treatment on subjects to observe responses, applicable in various fields like medicine.

Observational Study

• Focuses on measuring variables without controlling the environment, often used when experimentation is unethical.

Survey

• Investigates characteristics of a population through questionnaires directed at every member.

Simulation

• Employs models to replicate real-world situations, producing outcomes that mirror actual scenarios.
• Example: Rolling a die multiple times to predict outcomes.