What Are Descriptive Statistics? Definition, Tools, and Jobs

Many professionals use descriptive statistics to describe a large data set, give insight into data characteristics, and help businesses and organizations make informed decisions. Unlike inferential statistics, descriptive statistics don’t lead to inferences—instead, they provide you with a strong foundational understanding of your data set. You can continue to explore the ins and outs of descriptive statistics throughout this article, including different types of descriptive statistics, which industries use these techniques, and the pros and cons of this type of analysis.

What are descriptive statistics?

Descriptive statistics, such as mean, median, and range, help characterize a particular data set by summarizing it. It also organizes and presents that data in a way that allows you to interpret it. Descriptive statistics techniques can help describe a data set to an individual or organization and include measures related to the data’s frequency, positioning, variation, central tendency, etc. 

Descriptive statistics can help businesses decide where to focus further research. For example, suppose a brand ran descriptive statistics on the customers buying a specific product and saw that 90 percent were female. In that case, it may focus marketing efforts on reaching female demographics. 

Read more: What Is a Market Research Analyst? 2024 Guide

Inferential vs. descriptive statistics

Inferential and descriptive statistics are both ways you can characterize a data set, but they are best used for different purposes. You can use inferential statistics to learn about a population, including estimating parameters and testing hypotheses. When using inferential statistics, you may have a small random sample you can use to represent a larger population. 

Doing so allows you to test hypotheses and assess specific characteristics without having an extremely large set of data. On the other hand, descriptive statistics summarize data set characteristics without making assumptions about the underlying population.

Types of descriptive statistics

You can choose from several descriptive statistics measures depending on the data you would like to characterize. Some typical options include:

• Distribution: A data set's distribution includes the data's shape and spread. You may find that data is normal or skewed, which can tell you how the data points are spread.

• Central tendency: This represents where the center of the distribution lies and how the data spreads around it. Measures of central tendency often involve the mean (average value of the data points), median (middle score of the data), and mode (most common value of the data set). 

• Variability: The data variability includes the standard deviation, variance, and range of the data points. Each of these measures represents the dispersion of the data in the data set, showing how far away each value is from the average value of the data. 

Who uses descriptive statistics?

Professionals across many industries, such as finance, marketing, health care, business, sports, and social and behavioral sciences, use descriptive statistics. Some of the ways you might see this in different careers include:

• In the health care sector, descriptive statistics related to a particular patient or patient population might be used. Individual metrics such as blood pressure and heart rate or the average population characteristics related to risk factors for health outcomes might be examined.

• In the financial sector, you might showcase the variance of certain stock options and illustrate how volatile a specific type of investment is. 

• In data analytics, you may use descriptive statistics techniques to characterize raw data and present it in an easy-to-understand format that non-data professionals can interpret. 

Pros and cons of descriptive statistics

Descriptive statistics are just one way to characterize a data set. When choosing this method, being aware of the advantages and limitations can help you decide whether this method is suitable for you.

While the pros and cons will vary depending on your intended use, common advantages you might find include:

• Simple presentation: People with diverse backgrounds can easily understand descriptive statistics.

• Efficient summarization: Descriptive statistics allow you to characterize highly complex data sets into a few key numbers to give a quick overview.

• Graphical representations: Descriptive statistics can be easily visualized using bar charts, scatterplots, histograms, and other measures.

When it comes to limitations, keep the following potential disadvantages in mind:

• Reporting with no predictive abilities: Descriptive statistics report on what already happened. It doesn’t provide the context of why things happened or what it means for the future. In other words, you can’t generalize your findings to other populations or make inferences. 

• Potential for misinterpretations: Descriptive statistics are great for characterizing your data set, but you have to be careful with the scope and style of your representations. Sometimes, the visual or scale you choose may misrepresent the data.

What tools do people use for descriptive statistics?

While many descriptive statistics can be calculated by hand with smaller data sets, many professionals benefit from using statistical tools to validate their findings or streamline operations on larger datasets. 

Many of the following tools perform the same functions, so depending on your organization, you may have the option to choose the tool most comfortable for you. Some standard data tools for statistical analysis, including descriptive statistics, include:

• SAS: SAS is a commonly used statistical software that can perform descriptive, predictive, and prescriptive analytics.

• R: R is a free, open-source programming language that offers easy-to-use packages and libraries for performing descriptive and advanced data analytics. 

• Stata: Similar to R and SAS, Stata is a versatile software package that can perform descriptive analytics, data visualization, and data management.

• Prism: Prism is a software for data visualization and descriptive statistics, including scientific graphing. 

• OriginPro: OriginPro is a type of data analytics and graphing software that can generate many kinds of data visualizations. 

Jobs that require descriptive statistics

Professionals skilled in descriptive statistics are in demand across various industries. As you build descriptive statistics skills, you might have the skills needed to enter the following roles. 

Data analyst

Average annual base salary: $83,489 [1]

Data analysts collect, analyze, and describe data to help companies make business decisions. Descriptive statistics are essential tools for data analysts to summarize and visualize data effectively.

Read more: What Does a Data Analyst Do? Your Career Guide

Market research analyst

Average annual base salary: $79,145 [2]

Market research analysts collect and analyze data on market conditions, consumer behavior, and other organizations. Descriptive statistics help these professionals identify patterns and trends to inform marketing strategies.

Read more: What Is a Market Research Analyst? 2024 Guide

Statistician 

Average annual base salary: $99,087 [3]

Statisticians design surveys, experiments, and other data collection methods. After gathering data, statisticians analyze it and create descriptive statistics to draw reliable conclusions or to make data-driven guesses. 

Quality control analyst

Average annual base salary: $64,640 [4]

Quality control analysts use descriptive statistics to monitor and analyze manufacturing processes. Doing so allows them to ensure products meet specified standards.

Read more: Quality Assurance vs. Quality Control: Choosing the Right Career Path

Sports analyst 

Average annual base salary: $59,025 [5]

Sports analysts use descriptive statistics to analyze athlete performance. It also aids in informing coaching decisions, scouting, and game strategy.

Start learning with Coursera.

Descriptive statistics are an important starting point when analyzing your data. Looking at central tendency, distribution, and variance in your data set can help you understand underlying patterns and inform the next steps for your analysis. Whether you have a personal interest in statistics or would like to open your professional opportunities, taking courses on Coursera can help you build your skills and foundational knowledge. To start learning with basic statistical tools, consider completing Guided Projects such as Calculating Descriptive Statistics in R to build your portfolio and practice basic techniques.