Chapter 1 stat pt 1

Vocabulary flashcards covering key concepts from Chapter 1 notes: statistics basics, data types, populations/samples, levels of measurement, and descriptive vs. inferential statistics.

Statistics

The field of study focused on collecting, organizing, analyzing, and understanding numerical information from data.

Individuals

The people or items being studied.

Variable

A feature or characteristic of an individual that can be measured or observed.

Quantitative variable

A variable whose values are numbers that can be added or averaged, like height or age.

Qualitative (categorical) variable

A variable that puts individuals into groups or categories, like eye color or gender.

Population

The entire group of individuals that a study is interested in.

Sample

A smaller group chosen from the larger population to represent it.

Population parameter

A number that describes a characteristic of the entire population.

Sample statistic

A number that describes a characteristic of a sample (a smaller group).

Nominal level of measurement

Data that are just labels or names, with no specific order (e.g., types of fruit).

Ordinal level of measurement

Data that can be put in order, but the difference between values doesn't mean much (e.g., student rankings).

Interval level of measurement

Data that can be ordered, and the differences between values are meaningful, but there's no true zero point (e.g., temperature in Celsius (0^{\circ}C)).

Ratio level of measurement

Data that can be ordered, differences are meaningful, ratios are meaningful, and there's a true zero point (e.g., height or weight (0 means none)).

Highest level of measurement

The most precise type of measurement (nominal, ordinal, interval, or ratio) that fits a dataset, which tells you what calculations you can do.

Descriptive statistics

Ways to organize, display, and summarize data from a sample or population.

Inferential statistics

Methods that use sample data to make educated guesses or draw conclusions about a larger population.

Nominal data (example)

Data that are just names or categories with no order. Example: names of towns (Taos, Acoma).

Ordinal data (example)

Data that can be ranked or put in order, but the gaps between ranks aren't necessarily equal. Example: 1st, 2nd, 3rd place in a race.

Interval data (example)

Data that can be ordered, and the differences between values are meaningful, but zero doesn't mean 'nothing.' Example: body temperatures in Celsius (0^{\circ}C doesn't mean no temperature).

Ratio data (example)

Data that can be ordered, differences and ratios are meaningful, and zero truly means 'none.' Example: length of a fish in inches (0 inches means no length).