Now we’d like to explore two more data types — discrete and continuous — and help you understand the difference. (Then your organization might use statistical software to uncover insights from both types.)
The more you understand about these unique data types, the more you can identify opportunities where each might come in handy. Then you can leverage this information to benefit your brand whether you’re a data scientist, data analyst, data engineer — or just a fan of numbers.
Discrete vs continuous data
When looking at a set of numbers, they are typically discrete (countable) variables or continuous (measurable) variables. How you study this data should differ based on which group it falls into. This will certainly affect how it is measured as well.
What is the difference between discrete and continuous data?
Discrete data involves round, concrete numbers that are determined by counting. Continuous data involves complex numbers that are measured across a specific time interval.
A simple way to describe the difference between the two is to visualize a scatter plot graph vs. a line graph.
When you collect a set of round, defined numbers, they would fall in place on a graph something like those on the left. Discrete data relates to individual, countable items.
When you measure a certain stream of data with a complex range of results, these findings would be charted with a line as a range of data (see: graphs on the right). Continuous data relates to change over time, involving concepts that are not simply countable but require detailed measurements.
Hang tight as we open up these terms a bit more for better understanding.
What is discrete data?
Some synonyms for the word “discrete” include: disconnected, separate and distinct. These could easily be applied to the idea of discrete data.
We collect data to find relationships, trends and other concepts. For example, if you are keeping track of the number of push-ups you do every day for a month, an underlying goal is to evaluate your progress and the rate of improvement.
With that said, your daily tally is a discrete, isolated number. There is no clear-cut range as to how many you may do one day, so the relationship remains undefined. The more information you collect over time, the more insights you can deduce, such as that the average number of push-ups you did last week was 15 per day, which was 5 more per day than the week before. In the meantime, the numbers of push-ups themselves are whole, round numbers that cannot be broken down into smaller parts.
A fun rule of thumb is that, in many cases, discrete data can be preceded by “the number of.”
Examples of discrete data
Some examples of discrete data one might gather:
The number of customers who bought different items
The number of computers in each department
The number of items you buy at the grocery store each week
Discrete data can also be qualitative. The nationality you select on a form is a piece of discrete data. The nationalities of everyone at your job, when grouped together using spreadsheets software, can be valuable information when evaluating your hiring practices.
The national census is composed of discrete data, both qualitative and quantitative. Counting and collecting this identifying information deepens our understanding of the population. It helps us make predictions about the future while documenting history. This is a great example of the power of discrete data.
What is continuous data?
Continuous data refers to the unfixed number of possible measurements between two realistic points.
These numbers are not always clean and tidy like those found in discrete data, as they are usually collected from precise measurements. Over time, measuring a certain subject allows us to create a defined range, along which we can reasonably expect to collect more data.
Continuous data is all about accuracy. Variables in these data sets often carry decimal points, with the number to the right stretched out as far as possible. This level of detail is paramount for scientists, doctors and manufacturers, just to name a few.
Examples of continuous data
Some examples of continuous data include:
The weight of newborn babies
The daily wind speed
The temperature of a freezer
When you think of experiments or studies involving constant measurements, these likely involve continuous variables to some degree. If you’ve got a number like “2.86290” anywhere on a spreadsheet, this is not a number you could have easily arrived at yourself — think measurement devices like stopwatches, scales, thermometers and the like.
A task involving these tools probably applies to continuous data. For example, if we’re clocking every runner in the Olympics, the times would be contained on a graph along an applicable line. Even though over the years our athletes are getting faster and stronger, there should never be an outlier that corrupts the rest of the data. (Even Usain Bolt is only a couple of seconds faster than the historical field, when it comes down to it.)
There are infinite possibilities along this line (for example, 5.77 seconds, 5.772 seconds, 5.7699 seconds, etc.), but every new measurement will continually find itself somewhere within the range.
Not every example of continuous data will fall neatly into a straight line, but over time a range will become more apparent and you can bet on new data points sticking inside those parameters.
The importance of both continuous and discrete data
Just because we threw a “versus” in the title of this blog does not mean it’s a competition (although we won’t stop you from making “Team Discrete” or “Team Continuous” t-shirts).
The fact is, both types are equally valuable to data collectors, and you will encounter moments every day that give rise to measurements that could rightfully contribute to either data type. Any well-rounded research is formed by the marriage of these two unique groups of data.
Now that you know how to identify both, we hope you’ll have fun showing off these skills, whether name-dropping them with your colleagues or using this knowledge to inform your own research. And don’t sleep on the bounty of data on G2, generated from 800,000 software and services reviews (and counting) from verified professionals around the world.
Check out these free database software tools if you’re itching to collect and store some valuable data sets for your business.
Zangre is a Senior Research Specialist who helped with spearheading G2 Crowd’s expansion into B2B Services. He studied journalism at the University of North Florida — which is still undefeated in football — and joined G2 Crowd in 2016 when there was only one other “Andrew.” He has enjoyed contributing to newspapers and online publications while pursuing music and comedy projects in his free time.