# 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

Descriptive statistics is defined as a brief quantifiable summary that explains a given amount of data that represents a complete set of information or a sample of it. It is expressed mostly in numbers, which are concluded from a sample or an entire population.

There are three different ways of expressing this form of statistics.

- Frequency distribution

This consists of values that show the outcomes of different groups in an event. Similar responses or items are put together to form a group, and the number of subjects in a particular group becomes its quantitative summary, which can then be compared to other groups on the basis of frequency.

Here is an example to show students’ attendance in a class of 100.

Gender | Number |

Girls | 51 |

Boys | 45 |

Absentees | 4 |

This tabular representation points out that there are more girls than boys in the class.

- Measure of central tendency

Like the term “central” implies, this method focuses more on averaging a data set. This can be given using mode, mean, and median.

- Measure of variability

This is simply a degree of how spread out the subjects in a set are from each other. It is calculated using standard deviation, range, and spread.

## Definition of Inferential Statistics

Inferential statistics is defined as the information representation resulting from observing models of a population and making decisions on the basis of evidence and reasoning.

Basically, if you wish to find out useful information about a really large population, you either interview every subject individually, or you select a few representatives, known as samples, to cover for the whole population. This is one of the differences in descriptive vs inferential statistics to keep in mind.

From the description above, the second option is easier to work with. However, it is important to be mindful of what subjects you select to represent the whole population. The representatives must be chosen, not based on convenience, but based on analytical methods that account for sampling errors.

One of the verified approaches of obtaining samples that can be generalized to a population is the random selection procedure. In the inferential statistics vs descriptive comparison, it is apparent that the former offers easier methods of information gathering but not without some tradeoffs. It shows the result of a small cross-section of the actual population, which means there is a fairly significant possibility of inaccuracy.

## 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.