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Numerical data helps you test customer satisfaction & experience with your brand, products, services, and other aspect of the business in numbers which are easy to analyze. Let’s dive in to understand how numerical data is useful in research to accelerate business decision.
Numerical data refers to the data that is in the form of numbers, and not in any language or descriptive form. Often referred to as quantitative data, numerical data is collected in number form and stands different from any form of number data types due to its ability to be statistically and arithmetically calculated.
Example: You have a total count of your employees. You take a count of male employees and subtract that from the total number of employees to get the count of female employees. This characteristic of numerical data to be manipulated arithmetically makes it a best suit for statistical data analysis.
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The two forms of numerical data you will get to see are discrete data and continuous data. Both of these variants are explicitly used in statistical and research purposes and are proven to give the best data through research methods.
Let’s take a deeper look at how they are different from each other:
Discrete data is used to represent countable items. It can take both numerical and categorical forms and groups them into a list. This list can be finite or infinite too.
Discrete data basically takes countable numbers like 1, 2, 3, 4, 5, and so on. In case of infinity, these numbers will keep going on.
Example: counting sugar cubes from a jar is finite countable. But counting sugar cubes from all over the world is infinite countable.
As the name says, this form has data in the form of intervals. Or simply said, ranges. Continuous numerical data represents measurements and their intervals fall on a number line. Hence, it doesn’t involve taking counts of the items.
Example: in a school exam, students who scored 80%-100% come under distinction, 60%-80% have first class and below 60% are second class.
Continuous data is further divided into two categories: Interval and Ratio.
A numerical variable is something that inhibits any value that is finite or infinite. Like length, age, weight, exam scores, etc. numerical variables can be called a continuous variable when it has continuous data characteristics.
It has values with interpretable differences but never zero. These values can be added or subtracted but can never be multiplied or divided. Interval variables have standard difference between them and are an extension of ordinal variables.
Interval variables have two distributions: Normal distribution and non-normal distribution.
A random variable having a real value is said to be normally distributed when its distribution is unknown. Two different tests are carried on two different sample such as;
Matched sample tests
Unmatched sample tests
A random variable having a real value is said to be normally distributed when its distribution is known. Two different tests are carried on two different sample such as;
It is an extension of the interval variable but the difference is that it has a true zero value. These variables can undergo any operation like addition, subtraction, multiplication as well as division.
Talking about the tests that are performed on ratio variables, refer to those of interval variables as they are the same.
There are two ways to interpret the data collected in numerical form. Depending on your data and the way you results, you can make use of the following methods:
This sums up everything you need to know about numerical data before you start collecting them. Whether you use surveys or experiments to collect numerical data, it can help you get validate the hypothesis.
If you are ready to start collect numerical data but don’t know which method to use or what questions to ask you can contact our experts.