Two main kinds of statistics will be studied in this course:

1. Descriptive statistics: It consists of methods for organizing, displaying, and describing data using tables, graphs, and measures summarized.

2. Inference statistics: It consists of methods that use sample results to assist in decision-making, or in making predictions about a population.

Both kinds of statistics could be employed by the application of survey research. 

Imagine a snack bar would like the preferences of its clients between candy or salty food by carrying out survey research. One manner to make this is described in detail in the next Figure.

Definition of descriptive statistics elements

From Figure 1 it is possible to extract some important definitions:

• Population or target population: Consists of all elements – individuals, items, or objects – whose characteristics are being studied. The study population is also called the target population.

• Sample: A portion of the population selected for the study. If there are characteristics as close as possible to the population, then, is called representative, i.e., the same proportion of men and women, or even not having only people from the same city. In case all elements of the population have the chance to be selected, then the sample is said to be random. if this chance is equal for all elements, then the sample is simply random. If the elements were in alphabetical order and then selected, the sample would be non-random.

• Element or member: Represents a specific subject or object about which the information is obtained. For example, a person, a company, a country, an item, or a state.

• Variable: It corresponds to a characteristic under study that assumes different values.

• Observation or measurement: Value of a variable for an element.

• Data set: Set of measurements on one or more variables.

Types of Variables

The values of a variable could vary in its nature: quantitative or qualitative.

A quantitative variable means its values can be measured numerically and will be:

1. Discrete: 0, 1, 2, 3.

2. Continuous: 43.32 or [6.5; 7.8].

A qualitative variable means it describes data that fits into categories that may or may

not be sequential. For example:

1. Quality: Very good, good, bad, very bad.

2. States: Florida, New Jersey, Washington.

As a general rule, if you can apply some kind of math (like addition), it’s a quantitative

variable. Otherwise, it’s qualitative.

The next Figure  summarizes previous definitions of data extraction from an element or a member.