What is Descriptive Statistics? An Overview of its Importance

What is Descriptive Statistics? An Overview of its Importance | Data Science and Analytics | Emeritus

Descriptive statistics plays a crucial role in the business world. It helps organizations to make sense of the vast amounts of data they collect. It is used to make informed decisions based on the insights gained from the data. That is why organizations look for a statistician or a data scientist who is well-versed in market research, financial analysis, quality control, and other significant aspects of descriptive statistics. But what is descriptive statistics? How does it work? Dive into this blog to know more.

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What Do You Mean by Descriptive Statistics?

Descriptive statistics is a type of data analysis that helps to describe, show, or constructively summarize data points. This involves descriptive analysis, one of the most crucial steps in statistical data verification.

Moreover, it further provides marketers with a conclusion about the distribution of data. It also assists researchers in detecting typos and outliers that enable identifying similarities among variables, preparing them for further data analysis.

Descriptive statistics involves the use of several techniques, such as measures of central tendency (mean, median, and mode), measures of dispersion (range, variance, and standard deviation), and frequency distributions, among others.

1. Measures of Central Tendency

These values represent the center or typical value of a data set. The mean, median, and mode serve as the common measures of central tendency. Calculating the mean involves summing the values of the data set and dividing the result by the number of values. The median identifies the middle value when the data set is ordered. The mode is the value that occurs most frequently in the data set.

2. Measures of Dispersion

Measures of dispersion are statistical measures that describe the spread or variability of a dataset. Some common measures of dispersion include –

Range

Range, which refers to the exact difference between the highest and lowest values in the data set. 

Variance

The variance is a measure of how much the data deviates from the mean. It is calculated by taking the average of the squared differences between each data point and the mean. A higher variance indicates a wider spread of the data.

Standard Deviation

The square root of the variance is the standard deviation. Because it is in the same units as the data and is easier to read, it is frequently used to measure dispersion. A higher standard deviation shows that the data is spread out more.

3. Frequency Distributions

A frequency distribution is a table that summarizes the number of occurrences (frequency) of each value in a data set. This can be represented graphically using a histogram and a bar graph.

Histograms

A histogram refers to a bar graph representing the frequency distribution of a data set. The bars in the histogram represent the number of occurrences of each value, and the X-axis represents the values in the data set.

Bar Graphs

A bar graph refers to a graphical representation of all relevant categorical data. It consists of bars of equal width. The height of each bar represents the frequency or count of the corresponding category.

Why are Descriptive Statistics Used?

Descriptive statistical analysis surpasses other quantitative methods in comprehensiveness, offering a full depiction of an event or phenomenon. It employs quantitative analysis methods to portray the characteristics of a sample or population.

Visual representations like tables, charts, and graphs serve to illustrate descriptive statistics data. Additionally, it proves valuable in identifying variables and generating new hypotheses, which can subsequently be explored further through experimental and inferential studies. Another advantage of utilizing descriptive statistics is its minimal margin for error.

Importance of Descriptive Statistics 

Descriptive statistics facilitates data visualization. It enables data to be presented in a meaningful and understandable manner, allowing for a more simplified interpretation of the data set in question. The data available in raw form can be difficult to analyze, making determining trends and patterns that are difficult. Descriptive statistics makes it easy to understand complex data sets.

For example, a particular module has 100 students enrolled in it. Descriptive statistics can be used to determine the overall performance of students taking the respective module as well as the distribution of marks. Obtaining the marks as raw data would make it difficult to determine the overall performance. Furthermore, descriptive statistics enables a data set to be summarized and presented using a combination of tabulated and graphical descriptions. 

Types of Descriptive Statistics

Frequency Distribution

It refers to the number of times a specific incident occurs. It is recorded and denoted in a tabular format and used for qualitative and quantitative data analysis.

For example, assume that every year, a school takes a group of students to a picnic. Some of the students have already visited the picnic spot and are returning for the second time. A few others have gone to the picnic spot more than twice. 

Hence, the students are divided into groups based on the number of visits. As a result, the number of visits represents the frequency distribution among the students. 

Central Tendency

Central tendency is calculated using three methods: mean, median, and mode. 

  • Mean is the most common average value of an occurrence
  • Median is the data sample’s central or middle score
  • Mode is the most frequent value

For example, (taking the above-mentioned example into account) if three people visit the picnic spot on an average, the data mean value here is three. Among the various frequencies, two is the middle score for the number of visits and is thus regarded as the median.  

Variability

Variability describes how far apart data points are from one another. It also creates a range of dispersion and the degree of variance in the data sample from the highest to the lowest value. 

For example, the variability in the number of visitors to the picnic spot is an important factor to consider for park management because it can impact the park’s resources and staffing needs. It is, therefore, important for the park management to be aware of the variability in the number of visitors to the picnic spot over time and plan accordingly.

Descriptive Statistics vs. Inferential Statistics

Descriptive Statistics Inferential Statistics
Descriptive statistics is responsible for summarizing raw data information in a tabular format to test the hypothesis and for further statistical analysis.  Inferential statistics draws conclusions based on data collected using descriptive statistics.
Descriptive analysis is used for the meaningful representation of raw data. Inferential statistics compares test hypotheses and data, and makes predictions.
It takes into account small amounts of data. It used to extrapolate results (taken from descriptive statistics) to the entire population so it considers a large volume of data.
It is simply a representation of a situation. It is not only used to draw conclusions but it is also used by researchers to forecast possibilities, probabilities, and the occurrence of events.
Example of descriptive analysis:

A company collects information such as the number of sales, the average quantity purchased per transaction, and the average sale per day. All of this information is descriptive in the sense that it tells a story about what happened in the past. In this case, its purpose is to provide information.

Example of inferential analysis:

Based on the data, if the same company wants to launch a new product, then inferential analysis is used. It collects the same sales data, but it manages the data to forecast the sales of the new product. 

Based on the data, if the same company wants to launch a new product, then inferential analysis is used. It collects the same sales data, but it exploits the data to forecast the sales of the new product. 

 

ALSO READ: How to Learn Data Science: Is it Still All the Rage in 2022?

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There are 4.66 billion active internet users all across the globe. The amount of data generated is beyond one’s imagination. This trend has resulted in a high demand for data scientists who can use data to gain insight into the most appropriate and profitable business practices. Data scientists must prepare to take on larger roles in using technology to propel businesses forward. To do that, the right skill sets must be acquired. Here is where online data science courses will prove to be helpful in learning data science. These courses are created by a team of experts with key learning outcomes in mind, so you’ll be ready to take on and succeed in a data-driven career.

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About the Author

Content Writer, Emeritus Blog
Nikhil is a passionate and free-spirited writer with 4+ years of experience. He has a keen eye for the ever-evolving content landscape, which helps him craft captivating content across various genres. He writes about marketing, data science, and finance for the Emeritus Blog. Beyond work, Nikhil is a dedicated pet parent who loves leisurely walks with his beloved puppers.
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