Admin Published On Jul 21, 2023 | Updated on Sep 11, 2023 General

Before going over the differences, you need to understand the fundamentals of descriptive statistics vs inferential statistics. You can use both to analyse data efficiently. So, how are they different from each other?

First, let's answer a few questions.

What is Descriptive Statistics?

Descriptive statistics will help you analyse and summarise data in visual patterns. However, you will be unable to reach any conclusion or hypothesis from this type of statistics. Its function is only to help you analyse data and present it in a visually appealing format.

What is Inferential Statistics?

In inferential statistics, you take the data from a population and use it to make an inference; that is, you use this data to come to a conclusion or interpretation.

From the definitions of the two kinds of statistics, we can see an evident variation between descriptive statistics vs inferential statistics. Now, let's check out the differences between these two.

Descriptive Statistics vs Inferential Statistics: The Differences

When you're trying to figure out the essential differences between descriptive statistics and inferential statistics, it is vital to get a good idea about sample and population.

Population: It is a group of people or things from which you are to acquire your data. It can be human beings or any inanimate object.
Sample: Sample is a part of the population you can select randomly to make your study unbiased. A proper sample should be representative of all different kinds of people or things in the population.

Now that you know the essential characteristics of population and sample, let's see how these are important to understand descriptive statistics vs inferential statistics.

Difference in the main concerns

Descriptive statistics is responsible for giving you a description of the entire population. It focuses on the characteristics and ensures you have a visual representation of the target population.
Inferential statistics help you make inferences from a tiny sample of the population. You can use them to make a generalised hypothesis about the entire population from the data you receive.

Difference in functions

Descriptive statistics are merely concerned with presenting visual data from which you cannot get any inferences.
Inferential data helps you to test, compare and predict future outcomes based on the data you obtain from the ample.

Difference in final results

You can view the final results of descriptive statistical analysis in the form of visual graphs, pie charts, bar graphs, and tables.
The final results are in the form of probability scores in the case of inferential statistics.

Difference in tools of measurement

The mechanisms of measurement for descriptive statistics are measures of central tendency like mean, median and mode, and the spread of data like range and standard deviation.
In inferential statistics, the tools of measurement are hypothesis tests, confidence intervals and regression analysis.

Therefore, there is no doubt that there are glaring differences when we look at this descriptive statistics vs inferential statistics. Many students wonder which of these two branches of statistics they should use to analyse their data. The answer to this question depends upon the kind of project you're working on. Both of these branches have their pros and cons, which you can look at before choosing one of these methods.

Descriptive Statistics vs Inferential Statistics: Pros and Cons

When analysing your data, if you look at the differences between descriptive statistics vs inferential statistics, you'll find it hard to choose between the two. Both have their advantages and disadvantages. Here's an easy example of this:

Pros of Descriptive Statistics

Descriptive statistics has its fair share of advantages. Some of these are:

Presents data in a visually appealing manner, thereby increasing your understanding
Helps you interpret complex data more efficiently
Presents quantitative data in more manageable forms
Uses the entire population, reducing the risk of statistical error uncertainty

Cons of Descriptive Statistics

Despite its pros, descriptive statistics lack in some respects:

Descriptive statistics limits you from making any inferences or predictions from the data you obtain.
When you try to describe a large set of data using a single indicator, you might end up distorting the original data.

Pros of Inferential Statistics

Just like descriptive statistics, you'll find some advantages of inferential statistics as well.

Helps you draw conclusions from data
You can use the probability theory to make future predictions from the data sample.

Cons of Inferential Statistics

Even inferential statistics is not without its disadvantages.

You can never be entirely sure that the conclusion you draw from the data is accurate because you took only a tiny sample of the entire population.
You will need to make educated guesses in some places, which can influence the result's outcome

Therefore, from the pros and cons of descriptive statistics vs inferential statistics, you can see that both sides have their uses and faults. Now, your choice depends on the kind of assignment for which you need to analyse the data. If you're looking to form some conclusion from the data, it is better to go with inferential statistics. However, if you want to present complex data in a more straightforward visual method, descriptive statistics will help you out more.

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