What is quantitative data?

Quantitative data is countable and measurable. It's also a concrete fact. For example, if someone is 85, you cannot argue that they are 37. Quantitative data consists of a series of facts like age. 

Since it is numeric, math usually plays a role in telling a quantitative result. If you are tracking 100 people and 20 are 85 years old, 20% of your participants are 85 years old.

Quantitative data is anything that you can measure in definite units and numbers. There are many examples of quantitative data, including: 

• Revenue in dollars 

• Website page load speed 

• Time in seconds, minutes, or hours

• Distance in miles or kilometers 

• The results of a data-driven experiment 

• Website conversion rates 

• Age in months or years 

You can collect quantitative data through surveys. For instance, you could send out a survey about the average income in the households of Tulsa, Oklahoma, among a group of 500 people.

You’d be collecting quantitative data because salaries are inherently measurable. 

There are quite a few quantitative data collection methods, including: 

• Surveys 

• Questionnaires 

• Archival reviews 

• Structured observations

• In-app behavior data

• Analytics

Structured observations are an exceptionally immersive data collection method that can give you insights into a certain situation. For example, if you want to study how long the average school student’s screen time is, you need to create an accurate sample size.

First, it’s wise to figure out by which grade most students have cell phones. You’ll need permission to collect screen time data from a statistically relevant enough sample size of each grade. 

You’d then need to set a timeline for which the experiment will take place. It’s best to run this during a typical week since a big game, prom, or other events could skew your data.

Once your participants have shared their screen time data with you, it’s time to analyze the results. To pull all the data into one place for analysis, you might want to involve a developer to extract the data.

Visual representations such as graphs and pie charts are common result-sharing methods. A developer can create these with coding languages such as R or Python. If a developer isn’t on hand, you can use manual data entry to pull each participant’s data into one spreadsheet.

Once you’ve analyzed the data, you get to share the results. When you’re reporting the stats, remember that quantitative data is purely factual. It’s important to avoid generalizations and err on the side of caution. 

Too general, opinion-based: “Students at my school spend too most of their time on their phones.”

Much better at sticking to the facts: “70% of grade 8–12 students from Acadamy X spent 4 hours on their cell phones on average between 8–12 am during the period Aug 8–12, 2022.” 

People in STEM (science, technology, engineering, and mathematics) regularly use quantitative data. It answers the following questions: 

• What? 

• How many?

• How often? 

This form of data is quite beneficial for businesses and marketing. In relation to product management, UX design, or software engineering, quantitative data can refer to: 

• The rate of product adoption as a percentage of website visitors

• Conversions

• Page load speed

• In-app behaviors and percentage of users doing what behaviors

• Demographics and the percentage of users that hold each characteristic

• CAC (customer acquisition cost)

Quantitative data can help you keep track of your bottom line as a business. It makes it easier to stay on top of what’s happening across the board.

There are many ways to analyze quantitative data, and the method will likely vary based on your industry and goals. 

One example of analyzing quantitative data is Google Analytics. This is especially helpful for anyone who runs a website and wants to measure the following metrics: 

• The number of people that visited your site in the last day or week

• The length of each person’s average session 

• Where your traffic is coming from

This is only one way to analyze quantitative data, but doing so can be enormously beneficial to your business. 

Aside from Google Analytics, you can use many free tools to analyze quantitative data, including:

• Excel or Google Sheets

• Tableau

• RapidMiner

Many paid tools with more features also exist and include:

• Apache Superset

• GoodData

• Looker

Finally, those with programming skills can analyze data using programming languages such as:

• Python

• R

• SQL

A huge advantage of using quantitative data is that you’re presenting facts. As long as you have triple-checked your calculations to eliminate any errors, this is a wonderful way to prove a point. 

Numbers don't lie. If a notebook is flying off the shelves and you have sold $23,000 in stock over the past three months, that suggests it would make sense to invest in more of these notebooks. 

You could propose this to your team using irrefutable quantitative data and hopefully increase your business's revenue as a result. 

There are two types of quantitative data: Discrete and continuous. 

Discrete quantitative data is fixed-value information that you collect at a given time. It cares less about what happens over time and more about what percentage exists or the number at a specific point.

On the other hand, you measure continuous data across a period. 

Discrete quantitative data

Examples include: 

• An adult’s shoe size 

• The number of players on a team 

• The number of employees at a company 

These factors are consistent, not everchanging. You don’t measure discrete data over time because it only covers information for a specific event. For example, if there are six players on a basketball team for one season, that's discrete data. There could be seven or eight during the next season, but you’re only covering one event, so that’s what matters. 

Pie charts and bar graphs are usually the most effective way to represent discrete quantitative data. 

Continuous quantitative data

Examples include: 

• Website traffic 

• Water temperature 

• The time it takes to complete a task 

• Wind speed 

Maybe a product team has just introduced a new and highly requested feature and wants to measure how this has influenced the conversion rate. Here, we can take measurements from before the feature was launched and continually measure the conversion rate post-launch to see if the conversion rates grow. This is an example of continuous quantitative data. 

Line graphs are generally the most effective way to express continuous quantitative data. 

There are two subcategories of continuous data: Interval data and ratio data. 

Interval data 

Interval data is data that you can measure along a continuum. Temperature is one example because it moves above and below zero. 

Additionally, interval data is always numerical where there is a standardized and equal distance between two points. These numbers are called integers. 

Ratio data 

Ratio data is actually quite similar to interval data. The only difference is that it has a true zero. As we noted, temperature readings can be accurate and below zero. As there is no such thing as -1lbs, a scale uses ratio data. This is why it starts at 0.0 and cannot go any lower. 

Weight, length, height, and concentration are all examples of ratio data. 

In very simple terms, quantitative data refers to quantities, while qualitative data refers to qualities. 

Quantitative data refers to statistics, numbers, and measurements. 

Qualitative data refers to descriptions of people, places, and things—it describes specific attributes such as blue eyes or red flowers. Colors are qualitative data because they describe someone or something. 

For instance, you can conduct a quantitative survey measuring the number of Republicans and the number of Democrats residing in a particular state. To understand why they vote for a party, you’d have to gather qualitative data, such as their ideals, religions, motivations, and life experiences. 

The primary difference between qualitative and quantitative data is this: Quantitative data is a fact. If someone has earned $100,000 in the last 12 months, that is an inarguable fact, not an opinion. 

On the other hand, if someone is a Republican, that's qualitative: They may support right-wing policies, but that is just their opinion. People can argue on many political matters, and both would have solid points. The only irrefutable facts would come from quantitative data.

The simplest way to answer this question would be to find out exactly what questions you are trying to answer. Do you need to know the “what” or the “why”? 

Often in the digital product space, companies already have existing in-app behavior data and can likely answer the “what.” But if you don’t know “why” those numbers are what they are, that’s where qualitative data comes in. 

Still, this largely depends on the situation at hand: If you are running a company and want to understand how well a certain product is selling, it makes sense to calculate the number of customer sales over the past week or month using quantitative data. 

Gathering qualitative data will likely work in your favor if you would like to understand why people are not buying a specific product. For instance, if you sell books and you’re struggling to shift a specific title, you might want to ask your customers some qualitative questions: 

• What genres do you like?

• How long do you spend reading each month?

• Are there genres you don’t enjoy?

If your store attracts many people who love mystery novels but romance books never sell, you may need to conduct more qualitative market research. 

Of course, it will almost always work in your favor to gather qualitative and quantitative data so you can understand the what and the why. This will give you actionable data to address now and an understanding of what products or features your users want for future development. 

Quantitative and qualitative data are invaluable in different ways. Crunching concrete numbers will help you understand so much more about your business. And discovering what your customers want through qualitative data gathering makes it more likely that you’ll get it right the first time, saving time and money.