Descriptive and Inferential Statistics Vocabulary

Vocabulary flashcards covering fundamental statistical terms, variable classifications, and sampling methods.

Statistics

A way of reasoning, along with a collection of tools and methods, designed to help us understand the world.

Descriptive Statistics

A branch of statistics that consists of the collection, organization, summarization, and presentation of data.

Inferential Statistics

A branch of statistics that consists of generalizing from samples to populations, performing estimations and hypothesis tests, determining relationships among variables, and making predictions.

Variable

Characteristics whose values may differ by subject.

Data

The values of a variable.

Random Variables

A variable whose value is determine by chance.

Data Set

A collection of data.

Probability

The type of Statistics that describes the likelihood of an event.

Population

All subjects we want to learn about.

Sample

The part of the population who we get data from.

Hypothesis Testing

An inferential statistics process used to evaluate claims about a population.

Categorical Variables

Classified subjects or responses according to some characteristic or quality.

Nominal Data

Data that names or identifies, such as ID numbers, City, State, or Zip code.

Ordinal Data

Data that orders, but there is no clearly defined boundary, such as Military ranking, order by color, medals, or star rating.

Quantitative Variables

Variables measured or recoded with a number that corresponds to an amount.

Discrete Quantitative Variables

Countable variables, such as $(1, 2, 3, \text{...})$.

Continuous Quantitative Variables

Variables that include all values between any $2$ given numbers.

Strata

Groups created when dividing a population based on some characteristic.

Stratified Sampling

A sampling process where: Step one- population is divided into groups based on some characteristic; Step two- Randomly select subjects from each strata; Step three- Collect data from chosen subjects. Advantage: Guarantee representation.

Cluster

Groups that are mini versions of the population.

Cluster Sampling

A sampling process where: Step one- Divide the population into groups that are mini versions of the population; Step two- Select at least one cluster at random; Step three- Collect data from ALL subjects in the selected cluster. Advantage: faster, easier.

Simple Random Sampling (SRS)

A sampling method where: Step one- Number every subject in the population; Step two- Randomly generate the number of subjects needed; Step three- collect data from the corresponding subjects.

Systematic Sampling

A sampling method where: Step one- arrange population in some order; Step two- choosing random starting place; Step three- Collect data from every $n\text{-th}$ subject.

Convenience Sample

Collect data without any random process which leads to invalid results.