The difference between discrete data and continuous data is very important in Data Analytics, both have different classifications. Learn about the definitions, characteristics, and uses of discrete and continuous data in Data Analytics.
A number or fact that can be combined for statistical purposes. Then the data will be classified into two groups, namely quantitative and qualitative. Qualitative data types cannot be measured using numbers. While quantitative is data that can be measured using numerical properties. Furthermore, what is included as quantitative data is referred to as discrete data and continuous data. Even though it is included as quantitative data, it has a difference for its application, which is as follows.
What’s the Differences between Discrete Data vs Continuous?
Discrete Data vs Continuous Data are different types of quantitative data. These two data have different variables, discrete variables generally have values that can be counted while continuous variables have values that cannot be counted. Here are the main differences between discrete data and continuous data.
Discrete data is data that has a value limited to integers and only displays numbers that can be counted in integer amounts. While Continuous Data is data whose value can change continuously and is not limited to integers. Generally, discrete data is easier to calculate than continuous data, because continuous data is easier to measure. Discrete data is usually in the form of a graph or bar chart, while continuous data is more generally in the form of a line graph or histogram.
Both of these data are very important for companies, because they can produce information that benefits a business when processed and analyzed further. Data is one of the assets and must be maintained and utilized properly, to determine the performance of a company. To find out more details, here is the discussion
Example of Discrete Data and Continuous Data
The following are some of the example of Discrete Data and Continuous Data
Example of Discrete Data
Discrete values are taken from specific data, a finite amount of data that can be counted.
An example is a whole number that cannot be broken down into smaller numbers, in example:
- How many clients were there in the previous quarter?
- What is the size of the workforce in your sector?
- How many items are in stock?
- How many people are in a certain city?
Example of Continuous Data
Well, then for continuous data is data that can be measured.
Data whose value can be taken but has an unlimited amount and of course its value is not fixed, for example as follows:
- The weight or stature of a person.
- The height of a student
- Body temperature of a patient that is being treated.
- Daily weather report in your city.
- Can you determine the time to complete an activity?
Conclusion
It can be concluded that data involving discrete variables are quantities that have values that can be calculated with certainty, while data involving continuous variables are quantities that have values that cannot be calculated. In discrete variables, the variables loaded are usually limited values or certain integer values. Thus, when you make an assumption about the type of data, you must really understand the significance between the two data and the differences in general, namely:
- If discrete data is data that has clear points and space in it.
- It goes into a sequence and lives up to its name, which is continuous.
- For discrete data, it can be calculated easily.
- Meanwhile, continuous data is very difficult to assess.
- The value contained in discrete data is of course a separate value.
- For continuous data that includes all values use a range.
- Discrete data must use a graph and then for continuous data use a histogram.
- It can also be the frequency distribution on the data tabulation.
- For continuous data, the tabulation uses a group of values referred to as frequencies that can be classified.
That’s the difference between discrete data vs continuous data. It is important to understand this difference because the proper use of statistical methods depends on the type of data you have.
Editor : Meilina Eka A
