Discrete vs. Continuous Data: In statistics, data is all about facts and figures, which are collected together for the purpose of analysis. This data is divided into two main categories, quantitative and qualitative data. The quantitative data is further classified into discrete and continuous data. These types of data are valuable to data collectors. The main difference between discrete and continuous data is that discrete contains finite values, which contain nothing in between, while continuous can be measured and have decimals and fractions.
Let’s take a closer look at Continuous vs. Discrete Data
|Division||Discrete cannot be divided, while continuous data is sub-divisible|
|Countable||Discrete data is countable, while continuous is not|
|Granular||Discrete data is less granular than continuous data|
|Includes||Discrete data includes ordinal and integer values, while continuous includes quantitative data.|
Table of Contents
What Is Discrete Data?
If we look at the discrete meaning, it is the type of data which refers to countable, individualized items. But these items are un-divisible and only exist in units. Discrete variables have a limited number of values. In other words, they are finite. The main characteristic of discrete data is that it is “measurable” or countable”. Discrete data can easily be visualized with the help of histograms, bar graphs and other methods. Discrete data contains a wide range of other data types as well. For example, it handles qualitative data and ordinal data. Moreover, discrete data, for example, the letter grading system, does not have to be in number all the time. Discrete data also have limited granularity, and the users can only divide it into the smallest units.
What Is Continuous Data?
If we look at the continuous meaning, it refers to the type of data which is measurable. Continuous variables can also be divisible; in other words, these variables can go forever. Continuous data can easily be visualized in the form of trend lines, percentages, averages and line graphs. Continuous data is much more granular and specific as compared to discrete data. It is also considered to be more efficient and accurate as compared to discrete. The detailed physical measurement of continuous data like weight can be obtained from measurement tools. Analytics tools can also be helpful in providing more detail. Continuous data can be used in many different ways. For example, industrial and commercial use cases depend on accuracy to maximize efficiency.
7 Key Differences Between Discrete and Continuous Data
|Meaning||It is the type of data which refers to countable, individualized items. But these items are un-divisible and only exist in units. Discrete variables have a limited number of values.||It refers to the measurable type of data. Continuous variables can also be divisible; in other words, these variables can go forever.|
|Graphical Representation||The graphical representation of the discrete data is done through the bar graph.||The graphical representation of the discrete data is done through the histogram.|
|Values||It can only take separate or distinct values||It can take any value in some interval|
|Nature||The nature of discrete data is countable||The nature of continuous data is measurable|
|Tabulation||The tabulation of discrete data is known as Ungrouped frequency distribution.||The tabulation of continuous data is known as Grouped frequency distribution.|
|Function Graph||The function graph of discrete data shows isolated points||The function graph of continuous data shows a connected device|
|Examples||Number of students in the class, or number of the planets revolving around the sun||Height or weight of the students in a specific class or the number of stars in the space|
Discrete vs. Continuous Similarities
- Whether it is discrete or continuous data, both are equally valuable to the data collectors.
- Both of these data types are used in various measurements in our everyday routine.
- These two data sets can be combined together to form well-rounded research.
Discrete vs. Continuous Examples
- Number of students in a class
- A store’s inventory of computers
- Number of employees at a business
- Weight of an item
- Height of a person
- Product dimension
- Time spent on a website or project
Discrete vs. Continuous Pros and Cons
Discrete Data Pros and Cons
Pros of Discrete Data
- Discrete data is one of the most valuable resources for business because it refers to numerical information.
- Discrete data can provide a lot of information from a small amount of data.
Cons of Discrete Data
- Discrete data is not as detailed as continuous data, so it cannot provide much insight.
- Discrete data is sometimes harder to analyze because it cannot be divided into smaller pieces. It is not even precise as continuous data.
Continuous Data Pros and Cons
Pros of Continuous Data
- Continuous data is a preferred structure in most modern business cases. Due to its high efficiency and optimization, it is a must for a business.
- With the help of continuous data, you can have the relative utility of both in a professional setting.
Cons of Continuous Data
- Collecting continuous data is a very expensive process as it takes a lot of time.
- Some of the measurement tools can be restrictive; for example, some scales show a weight of 60 pounds even when it is 60.243 pounds.
There are two main types of data which you have often heard of, qualitative and quantitative. Qualitative data cannot be measured in numbers, while quantitative data can be measured in values and numerals. This quantitative data is sub-classified into further types, discrete and continuous. If we talk about discrete vs. continuous differences, both have different natures and representations. Discrete data is a countable type of data, while a continuous type of data is measurable. Both of these data types are used in various measurements in our everyday routine and are equally valuable to data collectors. But in Discrete Versus continuous accuracy, the continuous data are more accurate and efficient. Discrete data, on the other hand, do not have as much detail as continuous data, so it cannot provide much insight.