How does descriptive analysis fit in our daily life? We often hear the word “statistics” in our math classes. However, most of us aren’t aware of the importance of statistics in our life. It plays a big role in the medical field, quality testings, and even weather forecasting.

Before a political election, a study is often shown on news to help people be aware of who are the possible winners. To know an estimated population in an area, a case study often conducted instead of interviewing the location’s residence one by one. For you to know the target market for your business, you have to analyze who they are based on age, gender, and location.

Data analysis is the process of collecting, sorting and evaluating sets of data to come up with the information needed to help in making decisions through the application of statistical methods.

There are four main types of data analysis: descriptive analysis, diagnostic analysis, predictive analysis, and prescriptive analysis.

Descriptive analysis is called to be the foundation of all data insights. It answers the question, “what happened?” which makes it the simplest form among other types. Diagnostic analysis, on the other hand, answers the question “why did it happen?”. This type digs deeper to make a better analysis of what happened. The third type of data analysis answers the question, “what is likely to happen?” – the predictive analysis. It is used to determine patterns and predictions of future trends and outcomes based on the current data gathered. Lastly, the prescriptive analysis, combined with the insights from previous analyses, determines the course of action to take and finds the right solution to a problem.

For a simpler explanation of the types of data analysis, here’s an example:

When you came to a doctor for a check-up, the first question that he often asks is, “What do you feel?”. The next thing he would do is to either ask you more questions or perform some tests to help him understand your condition better. This will also help him determine what is likely to happen based on the test results. Finally, he’ll give you the prescriptions that you need.

In this article, let’s take a closer look at what descriptive analysis is.

## Purpose of descriptive analysis

The two main purpose of descriptive analysis:

**1. To measure the central tendency **

This is done to locate the center of your data. Many of us are already familiar with mean, median, and mode. These are the three most common measures of central tendency.

**a. Mean** – this is often called the average. To find the mean, you simply get the sum of all the data and divide it by the number of pieces in the set.

**Example**

52, 31, 12, 28, 26, 43

(52 + 31 + 12 + 28 + 26 + 43)/6

=192/6

The mean = 32

**b. Median** – the median is the number that is in the center of data sets. To calculate the median of a data set with an odd of data points, you simply get the data point in the middle.

**Example**

1, 2, 3, 4, **5**, 6, 7, 8, 9

If the data set has an even number of data points, you have to locate the two data points at the center and find its average.

1, 2, 3, 4, **5**, **6**, 7, 8, 9, 10

=(5+6)/2

The median = 5.5

**c. Mode** – it is the most commonly occurring value in a data set.

**Example**

{1, **2**, **2**, 3, 4, 5}

The mode = 2

In some cases, there are two repetitive values in a data set. That is called bimodal. If there are three same values in a data set, that is trimodal. For n modes, that data set is multimodal. Look at the sample below.

{**1**, **1**, 2, 3, **4**, **4**}

The mode = 1 and 4

This measure of central tendency helps in handling categorical data. For example, your ice cream shop sells 15 different flavors. For you to determine what flavor is the most popular, you need to find its mode, in this case, you need to check which flavor sells the most.

**2. To measure the spread of data**

Measures of spread describe the similarity or variability of a set of data. It includes the range, quartiles, variance, and deviation. It aims to give us an idea of how the mean represents data and to tell you how whether your data is tightly clustered or widely dispersed.

**a. Range** – it is the difference between the highest and lowest value in a data set. It is one of the simplest techniques used in descriptive analysis.

**Example**

9, 12, 65, 42, 18 (where the highest value is 65 and the lowest is 9)

= 65-9

Range = 56

**b. Standard Deviation**

Another way to measure the spread of data in descriptive analysis is through knowing the standard deviation. It is a measurement of the distance of the mean and the expected value. A low standard deviation means that the measure of data is tightly clustered while a high standard deviation means that the measure of data is widely spread out.

This measure of data dispersion is often applied to weather forecasting, stock market, and sports. This will help people who are planning to invest in stocks know the risk and give an idea of how much money they can earn or lose.

## Importance of Descriptive Analysis

Overall, the descriptive analysis focuses on showing sets of data in its simplest form. While keeping the raw and original data is important, analyzing and evaluating it in a way that will easily be absorbed by people especially when dealing with large sets of data. It can be applied in business such as when you are presenting a monthly revenue report or a target market overview. Instead of presenting raw data, they give a summarized data that is usually shown through pie or bar graphs. This type of data analysis plays a vital role in managing further analysis – whether you are creating an effective marketing strategy in your business or simply conducting research for your case study.

*Image by **Goumbik*