Defining Numerical Data (Importance and Comparison)

By Indeed Editorial Team

Published May 21, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Numerical data is quantitative information that comes in a number format, such as inventory, revenue, number of employees, and unique site visitors. Companies rely on numbers to provide an objective metric around which they can set goals and determine success. By understanding this data type, you can avoid subjective bias and create effective, reliable reports in the workplace. In this article, we define numerical data, discuss its importance, identify its characteristics, compare it to categorical data, and provide common examples of numerical and categorical information.

What is numerical data?

Numerical data, also referred to as quantitative data, is any information in number format. It differs from qualitative data, which is descriptive and provides the narrative of a study informed by numerical information. Numerical data comes in many forms, from the prevalence of a certain word in the text to the number of times a participant responds a certain way. For example, you can make a subjective observation that participants like cookies more than cake by watching which one they eat more. You can convert this into numeric data by listing how many bites the participants take of each option.

Importance of numerical data

Numbers ensure objectivity because they provide facts in a fixed manner. The numbers themselves remain unchanged by interpretation, and the different statistical models answer questions using the same data. Numerically structured data is important to establish standards by which to perform measurements. Without a specific way to quantify data, all research remains inherently subjective. Though bias impacts any research, whether factored into the study or unintentional, numbers provide measurable and interpretable data.

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Characteristics of number-based data

Number-based data has many identifying traits. These include:

  • Easy to manipulate and analyze: You can easily apply numerical information to any mathematical formula or statistical model. Math allows researchers to reformat numbers based on the analytic approach and desired information.

  • Usually estimated or enumerated: Numbers follow predictable, fixed patterns, allowing for researchers to make reasonable estimates. For instance, if interest adds up to 1.24865 dollars, it's standard to round that number to 1.25 dollars.

  • Features measurable intervals: Numerical information typically features fixed intervals that retain a constant value. For instance, the logarithmic pattern of the decibel system and the Richter earthquake scale both rely on fixed exponential intervals.

  • Allows for graphical visualization: There are many ways to display numerical data visually, ranging from scatterplots to histograms and bar graphs. Visual interpretation can provide context for a research claim, such as market trends and revenue patterns.

Numerical vs. categorical data

While raw numeric data provides quantifying information, categorical data sorts bulk information into different groups. These classifications support data analysis by narrowing down large sets of information into data that's pertinent to the research question. Categorical data is qualitative because it lacks a fixed metric, making it inherently subjective. For example, numeric data notes that the ice cream store has 10 litres of ice cream, while categorical data classifies it as either chocolate or strawberry. Categorical and numeric data types both provide important information, though there are significant differences between the two, including:

Data applications

Researchers typically rely on both quantitative and qualitative data. Numeric data provides measurable information that tracks important factors, such as the total number of people that walk in different neighbourhoods in a day. Categorical data distinguishes between the demographics of these walkers using parameters such as fitness level, age, and socioeconomic status. While subjective, it can reveal patterns in exercise behaviour that provincial health authorities can use to assess the potential benefits of adding walking paths in a certain area.

Analysis methods

Numeric data allows statistical analysis while categorical data doesn't support this type of mathematical function. Categories refer to data sorting. While they organize the numeric data, the category itself has no real value. To analyze categorical data, you can use methods such as a single-proportion z-test to express its proportion and frequency. Numeric data is less restrictive, letting you assess dependent and independent variables due to the standardized measurement system.

Data collection

Identifying the type of data you require can help you to determine the best approach to gather information for different purposes. If you seek qualitative information about car features, open-ended questions on a survey can be an effective option. Quantitative data collection, such as counting the number of vehicles that travel down a road in a set period, requires a different approach. Instead of mailing out a survey to vehicle owners, it's more effective to place a traffic count to obtain the raw travel data.

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Method of visualization

Categorical and numeric data benefit from different presentation techniques. For instance, if you plan to categorize the types of cars on the road, a pie chart can be an effective solution. Since the information is a proportion rather than a number, the pie circle represents a whole that serves as a visual frame of reference. Numeric data, such as the number of cars on the road during the day, is easy to represent on a scatterplot or line graph. The numbers are a fixed metric, serving as their own reference point.

Value of data

While categorical data can provide valuable information, it lacks the substance that numerical data provides. Researchers typically value categorical information for informing theories and generating hypotheses. Numeric data enables analysts to quantify those theories through a fixed experiment. The numbers provide an established approach that allows you to repeat the experiment, which is a necessary part of the scientific method.

An experiment can yield both categorical and numerical data. For example, consider a study that tracks the number of meals per day. While it can help to know that over half of your research participants eat dessert every day, the numerical value of the meal is more significant than the nature of the food itself.

Descriptive mechanisms

Categorical data works effectively with natural language, accepting responses such as "often" or "maybe." It has value, though the meanings of "often" and "maybe" differ between individuals. For instance, if research participants respond that they would often purchase an item, it could mean once a year for one participant and once a day for the other. Because of this subjectivity, you can't assign numerical values to categorical data, since there is no established way to calculate it.

Numeric datasets rely exclusively on numbers and function effectively in any viable mathematical assessment. To describe this type of information, you can use different measurements. Common examples include the dollar value of a company's annual revenue, its number of employees, and its physical office space in square meters.

Read more: Types of Variables in Statistics and Research (With FAQs)

Data structure

Numeric data is consistently structured by virtue of its metrics. There is software available to structure these numbers, including databases and spreadsheets. It allows for efficient access to the information and secure storage. Categorical structures are artificial, where you can apply them to databases to organize the information and reveal patterns. Unstructured categorical information doesn't rely on statistical analysis but instead offers context.

For instance, consider a town performing a study to determine if widening a narrow road is a good idea by placing a camera and a traffic counter on the road. Numeric data can tell you that 1,000 vehicles travelled on a certain street on a certain day. Structured categorical data can tell you that 800 of these vehicles were trucks. Unstructured categorical data can tell you that 435 of the vehicles have red paint. Each piece of information has value, but the high ratio of trucks is most relevant to the study because they require more space than cars.

Examples of numeric data

Anything that has fixed, countable units qualifies as numeric data. Any measurement results in a numerical value, whether it's the size in centimetres, time in days, or the number of repetitions. Because of this prevalence, it results in bulk information that relies on categorization to add context and value. There are several examples of numerical information, including:

  • Annual income

  • Population statistics

  • Birth rates

  • Death rates

  • Tax rates

  • Business revenue

  • Interest rates

  • Gross domestic product

  • Number of employees

  • Grade point averages

  • Number of family members

  • Value of bank accounts

Examples of categorical data

Qualitative information gives numerical data purpose by sorting it into usable facts that answer specific questions. While subjective, the details it provides lend insight into the population and assist in identifying potential bias by incorporating demographic and psychographic data. Categorical information comes in many forms, including:

  • Political affiliation

  • Hair colour

  • Favourite actor

  • Level of fitness

  • Health conditions

  • Province of birth

  • Fashion sense

  • Preferred type of art

  • Ethical philosophy

  • Age and ethnicity

  • Preferred type of car

  • Dream career

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