Difference Between Descriptive and Inferential Statistics
In the world today, life without statistics is just unimaginable because almost everything that we do depends on it. Take, for instance, a rich father who has no idea the number of kids he has should not be surprised if he is told that a young beggar on the street is one of his sons. Well, that’s simply because he has no idea the number of kids that he has. Indeed, this buttresses the point that stats are used for planning.
To make the most of the data generated from field, it is grouped into usable formats for effective decision making. In fact, our world has grown so attached to it so much so that we cannot do anything without it. From a broad prism, the study of stats is divided into two: descriptive and inferential statistics. Well, the next question that may be going through your mind now is: what is the difference between descriptive and inferential statistics? You see, we are assuming that is the reason you are reading this piece in the first place.
However, the good news is that we will walk you through the difference descriptive and inferential statistics. But just so we don’t put the cart before the horse, we will first and foremost launch this well-researched article with their meanings.
Definition of Descriptive Statistics
In a nutshell, this is a branch of stats that deals with a concise analysis of data, which summarizes the either a sample or the whole data itself. From the information, a pattern is likely going to emerge which the statistician will use for one purpose or another.
This means that there is a conclusion reached because the user has achieved a particular objective from the sample. When conclusions are drawn through this means, the information becomes so easy to use. More importantly, anyone can have access it to and use for many different purposes. Oftentimes, this is the process used in calculating statistical problems in the academic community and other places where they are relevant.
For instance, the information can be further tabulated or represented on a graph/charts. This remains one of the most common ways of representing stats in today’s industry.
You may now wonder: What then is the difference between descriptive and inferential statistics? Just before we delve into that, let’s explain the meaning of the other term.
Definition of Inferential Statistics
Essentially, this has to do with making projections or assumptions from an analyzed record. In other words, the statistician arrives at a particular decision after testing a hypothesis that may not actually be true. Afterwards, the person goes further to make projections or estimations of what could be or what could happen in the future based on the records available.
A typical example is when someone stands at the entrance of a mall to carry out a simple survey. As you would expect, the mall has lots of stores in it. Now, before a shopper goes into the mall, the person will find out from the shoppers the actual store that he wishes to buy from. Let’s say that he took a survey of 100 shoppers. Of the total number of shoppers, 50% visited Store A, which represents the highest number of shoppers who visited the entire mall per store.
From the records analyzed, the statistician could generalize that of total number of shoppers who usually visit the mall, 50% of them go to Store A. Notice that this is a present continuous tense. This simply entails that the figure arrived at from these stats has been used to make a general statement, which could potentially be FALSE as the number of shoppers who buy from a store depend on a number of factors.
To clarify the disparity between inferential and descriptive statistics, the instance cited above will be used throughout this guide.
Descriptive vs Inferential Statistics Comparison Table
What is the difference between descriptive and inferential statistics? Well, the table below has the answer to that question.
|Basis of Comparison||Inferential||Descriptive|
|Meaning||Data generated and analyzed, which is later used for making assumptions and projections.||Data generated and analyzed, which is used to draw real conclusions|
|Acceptability||This is vague||This is factual|
|Technique||Hypothesis test and analysis of variance||Use of central tendency and data spread|
|Relevance||For getting likely ideas and future projections||For immediate use|
|Final Result||Put into probability scores||Put into charts, tables and graphs|
Basically, the table above clear shows the difference between descriptive and inferential statistics. At this juncture, we will go ahead to conclude this disparity between the two mathematical techniques.
Conclusion of the Main Difference Between Descriptive vs Inferential Statistics
Now the question goes again: “What is the difference between inferential and descriptive statistics?” At this point, we can say that we have answered that question to a large extent. However, for those who need further clarifications, we will refer to the instance that we cited earlier. Hopefully, the examples of descriptive and inferential statistics will help many readers understand it better. So, we will return to it shortly.
We earlier mentioned a situation where statistician has to stand at the entrance of a mall to carry out a survey. Remember? Great! Given that the person got 50% as the number of shoppers who used Store A, it then means that of the 100 shoppers who visited the mall, 50 buyers went to Store A. Well, this 50 that the person arrived at is the descriptive stats. On the other hand, just like we stated earlier, the projections that he makes from the stats arrived at is classified under inferential stats.
Therefore, this means that the statistician has generalized that any day you go to that mall, just assume that 50% of the people who buy from the mall go to Store A. This, with all intents and purposes, could be FALSE. Wrapping up, we firmly believe that the descriptive and inferential statistics examples give you an in-depth grasp of the difference between inferential and descriptive statistics. Keep in mind, however, that the former is merely used for making estimates – nobody takes it seriously as decisions made from it cannot stand.
Indeed, it is just for assumptions regardless of the number of times the survey was carried out and the mean is used to arrive at the final assumption. At this point, we encourage you to share the knowledge with others.