It’s no secret that data rules the world. There’s the data that businesses collect to make better decisions. Big data that conglomerates use to predict trends in the market and maintain their algorithms. And, there’s everything else in between.
Data is the lifeblood of society but there are different types. Categorical data, numerical data, primary data, etc. In this guide, we’re going to dive deep into what numerical data is, the different types, how it can be used, and much more.
Numerical data definition
Numerical data has a name that’s truly descriptive. It refers to data that’s in the form of numbers and is not categorized using descriptive text or natural language. It can be used to do statistical analysis and arithmetic.
There are countless instances of numerical data in our daily lives and for business use cases. For example, you buy a crate of eggs. After purchase, you count the total number of eggs you have. You use a few to make breakfast. Instead of recounting the entire crate, you subtract the number you took from the original amount to arrive at the number of eggs remaining.
The example above was simple but, that’s what it is at its core. It can be a parameter, a statistic, an average, a mode, a range of numbers, etc.
How is numerical data used?
To be frank, the breadth of use cases for numerical data is simply too wide to cover here. Instead, we’ll just give you a few examples of use cases so you can better understand how numerical data can be used.
- Interstellar travel – Many people may not realize it but one of the greatest limiting factors of traveling through deep space is the ability to make accurate calculations. Even if you’re off by just a tiny fraction of a fraction, it could result in veering off course by millions of miles or a critical system malfunctioning.
- Census – Countries need to understand the number of people living within their borders, the breakdown of those people (ethnicity, family size, income, the population in an area, etc.), and things like income level. This will help policymakers decide on the long-term direction of the nation or even their constituency.
- Business financial departments
- Price sensitivity analysis – This is the method of learning how much your customers are willing to pay for products and services. The analysis is carried out using a combination of numerical data (how much people will pay and how many people will pay that), and categorical data (why would you pay that amount and what category does this person fall into).
- Healthcare – This impacts urban planning because people shouldn’t live so densely without proper amenities, government policies such as what kind of roads to build, projects to focus on, sanitation, etc. This is based on total population and population density. It also impacts what medicines are researched and eventually get brought to market based on the cost to produce, the population affected, and more factors that can be determined based on numerical data.
Types of numerical data
There are multiple types of numerical data and each is ideal for certain types of situations. They can be largely divided into two categories which also have subcategories.
Discrete numerical data
This is a type of data that you can count and or divide into parts. Each unit has a unique value that is generally fixed. The data can be finite but it can also be infinitely countable. Meaning it can be counted but you’ll never be able to count all of them. An example of an infinitely countable value would be the grains of sand on a beach. An example of a finitely countable number would be the number of cars in a city.
The main difference between the two is that one has a beginning and an end (finite) and the other has a beginning but no clear end (infinite).
Continuous numerical data.
Continuous data is a type of numerical data that occurs as units on an interval scale as opposed to counting like with discreet data. The values can change over time based on different factors or conditions which makes the scale so important.
The numbers aren’t fixed so the scale serves as a reference point for others to understand what the value means. For example, temperature is considered continuous numerical data. The temperature in a home can fluctuate depending on the location, materials used to build it, altitude, time of day, etc. It’s not a fixed value but changes over time. There are two types of continuous data.
- Ratio data – Ratio data is measured using a scale with equidistant points between each value. The scale has a zero point but that doesn’t necessarily show the absence of the value being measured. Temperature is a good example of this. There’s a zero on the scale but points below zero can also be measured.
- Interval data – This is continuous numerical data that’s measured using a scale. The values on the scale have the same distance separating each other so comparisons can be made. What’s unique about interval data is that it doesn’t have a zero point.
Key characteristics of numerical data
- Can perform statistical analysis – One of the greatest benefits of numerical data is the ability to look at it from different angles. You can combine multiple data sets and analyze them in new and unique ways.
- Equidistant units – Usually, the units on a scale – whether it’s a ratio or interval scale – are the same distance apart. This makes it easier for you to understand the magnitude of a change. For example, every degree on the Celsius scale has the same magnitude so it’s easier to understand a change of 20 degrees and a change of 100 degrees.
- Simple to visualize the data – Another distinct advantage of numerical data is the ability to visualize it using multiple representations. This includes bar graphs, pie charts, line graphs, etc. The data is also easier to compare when visualized.
These are just a few of the characteristics you should be on the lookout for when working with numerical data.
Difference between categorical data and numerical data
Both categorical data and numerical data have their strengths and each can be used for analysis when trying to answer important questions. They also have distinct differences and each one is ideal in certain situations.
Categorical data is information that’s separated into multiple categories based on qualitative characteristics. Instead of using numbers, it’s characterized by natural language and descriptions. When numbers are used for categorical data, they don’t have a meaning beyond the description they’re providing and statistical analysis cannot be used.
There are many differences between the two types of data.
- How the data is arranged – Categorical data doesn’t have any built-in ways to arrange it so it’s usually grouped based on topic, use case, or other factors that matter to the researcher. Numerical data has natural scales which can be used to group them by default.
- How numbers are used – When categorical data has numbers, they’re used for their descriptive properties. For example, a postal code. Numerical data uses numbers as a representation of some value and statistical analysis can be performed with those values.
- Data collection methods – Numerical data usually takes advantage of close ended questions while categorical data takes advantage of open-ended questions.
Analysis of numerical data
There are two major schools of thought when it comes to analyzing numerical data. Each one has its own advantages, disadvantages, and ideal use case. The first one is descriptive statistics and the second one is inferential statistics.
As the name implies, this type of analysis uses data collected from a sample of a specific group or population and uses it to describe that same group or population. For example, if you use quota sampling to collect information about residents of Phoenix Arizona then you’ll use that same data to do statistical analysis related to residents of Phoenix Arizona. The analysis encompasses mean, median, variance, standard deviation, and more.
With this type of analysis, you make inferences or predictions about a population based on the data you’ve collected from a sample of that population. You collect data from 10% of the population and make predictions about the other 90% of the population. The type of analysis that can be done include SWOT analysis, trend analysis, conjoint analysis, and more.
With descriptive analysis, you’re trying to understand information about the population right now. This can be ideal when you’re putting together a statistical report. With inferential statistical analysis, you’re trying to understand how the population will develop in the future and make decisions accordingly. This is ideal for policy planning and business decisions.
Examples of numerical data
If you know what you’re looking for, you’ll realize that there are countless examples of numerical data. Here are just a few:
- The census – Discrete numerical data
- Measures of time – Continuous numerical data
- Temperature – Continuous numerical data
- Weight – continuous numerical data
- Height – continuous numerical data
- Company revenue – discrete numerical data
As you can see, numerical data is common and can be used in a wide variety of situations. It’s up to you to understand what type of numerical data you’re interacting with or that you need and take the right actions to better analyze it and present your conclusions.
Start with simple discrete numerical data and descriptive analysis then work your way up to more complex data sets. Over time, you’ll be able to extract deep insights and make better decisions as a result.