Notes on Data Concepts: Elements, Population, Variables, Samples, and Nominal Data

Data, Information, and the Data Landscape

Remember, understand, apply, analyze, and evaluate data and data sources. This reflects the cognitive process (Bloom's taxonomy) for working with data.
By data, we mean the facts and figures collected, analyzed, and summarized for presentation and interpretation. It's information, and when we have information we want to learn as much as we can from it.

Element: the entities on which data are collected. Example: degree candidates.
Population: the set of all elements of interest. Example: all degree candidates worldwide.
Variable: a characteristic of interest for the elements. Examples: age, height, or weight.

Populations can get pretty large. Example given: there are about 7{,}970{,}000{,}000 people in this world.
Because populations can be enormous, we typically deal with a sample, which is a subset of the population.
Example: degree candidates enrolled in Business Statistics Fundamentals for Managers.

The source of our data can come from two types of data. The transcript explicitly discusses qualitative data (also called categorical data) first.
Qualitative data are referred to as nominal data in this context.

Nominal data are simply labels.
Example: a bank, a credit union, or a savings and loan. They all basically provide the same function, but there are differences among them.
None are bad, none are good. The speaker emphasizes labeling distinctions without value judgments.

The material highlights the basic taxonomy of data types (data vs information) and the distinction between elements, populations, samples, variables, and qualitative/categorical data with nominal labels.
Real-world relevance: in managerial statistics, knowing when to collect data from a full population versus a sample, and how to classify data (especially nominal categories like institutions) is foundational for analysis.

Data are facts and figures used to learn from information.
An element is a unit of observation; a population is the set of all elements of interest; a variable is a characteristic measured on those elements.
Populations can be very large; sampling is often necessary to draw inferences.
Data types include qualitative (categorical) data; nominal data are labels used to name categories.
Examples illustrate that even when categories serve the same function, they can differ in important ways; all categories are neutral placeholders in this framework.