Statistics
04.01.2023 • 4 min read
Subject Matter Expert
This article goes over what types of data in statistics exist, their importance, and several examples.
In This Article
In statistics, data are the raw materials we use in our analysis. Data are the pieces of information, such as labels or numbers, we use to test hypotheses and make predictions.
We can divide data into two broad categories: qualitative and quantitative.
We can further divide these two categories into:
Nominal
Ordinal
Discrete
Continuous
Qualitative data represent labels or characteristics that divide your overall data set into distinct groups or categories.
Nominal data are qualitative data with no inherent ranking based on size or magnitude.
Example - Nationality
American
Mexican
Canadian
Ordinal data are qualitative data with an order based on size or magnitude.
Example - Self-reported happiness of survey respondents
Very unhappy
Somewhat unhappy
Neutral
Somewhat happy
Very happy
Quantitative data consists of counts or measurements.
Discrete data are quantitative data representing distinct countable values. Discrete data usually takes the form of whole numbers or integers.
Example - The number of customers who visit a coffee shop each hour.
Continuous data are quantitative data where the data can take on decimals or fractional values. They represent measurements. Continuous data can take on an infinite set of values within a range. You usually need a measuring device like a stopwatch or scale to collect continuous data.
Example - The time it takes runners to complete a half-marathon.
The table describes qualitative data and provides some additional examples of the two types of qualitative data: nominal and ordinal
The table describes qualitative data and provides some additional examples of the two types of quantitative data: discrete and continuous.
The four main types of statistical data are:
Ordinal data
Nominal data
Discrete data
Continuous data
Ordinal and nominal data both fall under the category of qualitative (or categorical) data. Discrete and continuous data fall under the category of quantitative (or numerical) data.
It’s easy to mistake some types of qualitative data with discrete numerical data. Certain types of qualitative data may take the form of a numerical value, but all that number does is classify your data into groups.
For example, the U.S. State Department issues travel advisory warnings on a scale of 1-4. Where Level 1 urges travelers to exercise some caution while traveling, and Level 4 urges people not to travel to certain destinations. These numbers are discrete values, but all they do is divide travel warnings into categories ranked by severity. The numbers do not count or measure anything.
Age is a good example of a continuous variable that is treated as a discrete variable. Technically, we can measure a person’s age in fractions of a year. This means a person’s age can take on an infinite number of possible values. However, we are typically only concerned with age measured in years; therefore, we treat age as a discrete variable.
Certain types of data visualizations, such as bar graphs, pie charts, and frequency tables, are particularly useful for visualizing qualitative data.
Histograms, dot plots, box plots, and frequency distributions are great for visualizing quantitative data.
Proportions and percentages are quantitative, but they can also bridge qualitative and quantitative data. Percentages allow you to convert qualitative values into quantitative values.
For example, say you have the political affiliation of 1000 voters. The political affiliation (Republican, Democrat, Independent) is a categorical variable, but by taking the proportion of voters who fall into each category, you can convert your data into a numerical variable.
In addition to categorizing data as quantitative or qualitative, statisticians and data scientists also categorize data by data collection methods. Statistical data that has been collected through a carefully controlled randomized experiment is called experimental data. Data collected through passive observation are called observational data.
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