Types of Data Module One Lesson Two

A set of vocabulary flashcards covering the classification of data by type and level of measurement, as well as biological and botanical classification examples mentioned in the lecture.

Data

The set of actual responses or values obtained when asking about a particular variable.

Qualitative Data

Also called categorical data, this involves labels or descriptions of traits using words or phrases and is typically not used in calculations.

Quantitative Data

Data consisting of counts or measurements expressed as numbers with which calculations can be performed.

Discrete Data

A type of quantitative data that is the result of counting and involves whole units without fractions or decimals.

Continuous Data

A type of quantitative data resulting from measuring that can assume an infinite number of values between any two specific numbers and may involve fractions or decimals.

Nominal Data

A level of measurement for qualitative data only that classifies data into mutually exclusive groups with no sense of order or ranking.

Ordinal Data

A level of measurement for qualitative data that classifies data into mutually exclusive groups with a sense of order or ranking, though exact differences are subjective.

Interval Data

A level of measurement for quantitative data where exact, objective differences between values exist, but there is no true zero (e.g., $0^{\circ}$ does not mean the absence of heat).

Ratio Data

A level of measurement for quantitative data where exact, objective differences exist and there is a true zero, implying the absence of the trait being measured.

Kingdom, Phylum, Class, Order, Family, Genera, Species

The hierarchy used in Biology to classify or group animals, with the example Species provided as T. Turncatus.

Angiosperms

A classification of plants that have roots, stems, leaves, and make seeds via flowers.

Gymnosperms

A classification of plants, such as conifers, that have roots, stems, and leaves and make seeds but have no flowers.

Nominal Examples

Data types such as Eye Color, ID numbers, Gender, Ethnicity, Political Affiliation, and Zip Codes.

Interval Examples

Specific quantitative measures such as temperature, calendar dates, and IQ scores.

Ratio Examples

Quantitative data including Height, Weight, Salary, Exam score, and Number of phone calls in a day.