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What are Descriptive Statistics, and Why are They Useful?

Kaeli Gardner, Yolanda Williams, Jerry Allison
  • Author
    Kaeli Gardner

    Kaeli B Gardner (pronouns: she/her) completed a BS in Mathematics in 2016, and a MS in Mathematics in 2018, both at East Tennessee State University. An East Tennessee native, she teaches mathematics at the high school and college levels for several schools.

  • Instructor
    Yolanda Williams

    Yolanda has taught college Psychology and Ethics, and has a doctorate of philosophy in counselor education and supervision.

  • Expert Contributor
    Jerry Allison

    Jerry holds a Doctor of Business Administration and a Master’s in Mathematics. He has taught business, math, and accounting for over 25 years.

What is Descriptive Statistics? Learn about the descriptive statistics definition and its purpose. See the descriptive statistics examples and its types. Updated: 10/01/2021

What Is Descriptive Statistics?

In the study of statistics, there are two main branches: descriptive and inferential. The main difference is that descriptive describes a data set as it is, and inferential attempts to make predictions, which goes beyond the values in the data set.

What Is the Purpose of Descriptive Statistics?

As mentioned previously, descriptive statistics refers to various statistical calculations that are used to describe a data set as it appears. That's the meaning of descriptive statistics, but what is the purpose of descriptive statistics? One common example in sports would be the batting average. It is a value calculated considering every instance of a player taking their place at bat, which describes the average proportion of times when the player scores a hit. That's descriptive statistics that most people encounter quite frequently. Another example is the grade point average. It is a statistic that converts letter grades into numerical values and calculates a weighted average based upon the number of credits a course is worth. Descriptive statistics help quantitative research enormously, as they quantify some key aspects of data for direct comparison and easy conclusions.

What Are Descriptive Statistics?

Imagine that you are interested in measuring the level of anxiety of college students during finals week in one of your courses. You have 11 study participants rate their level of anxiety on a scale from 1 to 10, with 1 being 'no anxiety' and 10 being 'extremely anxious.' You collect the ratings and review them. The ratings are 8, 4, 9, 3, 5, 8, 6, 6, 7, 8, and 10. Your teacher asks you for a summary of your findings. How do you summarize this data? One way we could do this is by using descriptive statistics.

Descriptive statistics are used to describe or summarize data in ways that are meaningful and useful. For example, it would not be useful to know that all of the participants in our example wore blue shoes. However, it would be useful to know how spread out their anxiety ratings were. Descriptive statistics is at the heart of all quantitative analysis.

So how do we describe data? There are two ways: measures of central tendency and measures of variability, or dispersion.

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Descriptive Statistics Examples

With the above examples of batting average and grade point average, one can see few more examples of descriptive statistics. Here's a little more abstract example: consider the data set {2, 5, 7, 6, 8, 9, 5, 7, 10, 4}. What is the mean of this data set?

The mean referred to here is the average of a data set, and it is calculated by taking the sum of the data set and dividing that sum by the size of the data set. In other words, for a data set containing n elements,

$$\overline{x} = \frac{\sum x}{n} $$,

where x is an element of the data set, and n is the number of elements in the data set. Such mean is known as{eq}\overline{x} {/eq}, pronounced "x-bar," and it is one of the most common and useful descriptive statistics.

For this data set, the mean is:

$$\overline{x} = \frac{\sum x}{n} = \frac{63}{10} = 6.3 $$

Types of Descriptive Statistics

There are two types of descriptive statistics: measures of central tendency, also called measures of center, and measures of dispersion, also called measures of variability or spread. The former describes the value(s) which the data set seems to be clustered about, while the latter describes how to spread out the data is. By considering the two together, one can determine a "typical" value for the data set. They can also know how far away from that typical value a data point is likely to be.

There are four kinds of descriptive statistics: measures of frequency, measures of central tendency, _measures of dispersion, and measures of position. In this article, the focus is mainly on measures of central tendency and dispersion. Measures of frequency are concerned with how many items there are in data sets. These statistics include frequency, or counts, and relative frequency or proportions. Measures of the position include percentile rank and quartile rank (which is itself a subset of percentile rank).

Now, take a closer look at measures of central tendency and measures of dispersion:

Measures of Central Tendency

The measures of central tendency in statistics refer to the "middle" or "average" of a data set. There are three measures of central tendency, which are used in statistics:

The three measures of central tendency are:

  • Mean - the average of a data set
  • Median - the middle of a data set
  • Mode - the value which appears most often in a data set

Example 1. Consider the data set {2,3,3,4,5,5,6,7,7,7,8}. Find the mean, median, and mode.

The mean of the data set is its average:

$$\overline{x} = \frac{\sum x}{n} = \frac{57}{11} \approx 5.18 $$

How to find the median. Source: Wikimedia Commons

Example showing how to find the median of a data set containing odd and even numbers of elements

The median of the data set is the value in the middle. The data set is already in numerical order, so one needs to do is to find the middle term. There are 11 elements in the set where the middle will be the one data point with as many terms before and after it:

$$2,3,3,4,5, \color{red}5, 6,7,7,7,8 $$

The middle value is 5, so the median is 5. Compare this to the mean, which is 5.18. The mean is a little higher. Why?

Let's look at the mode for a clue. The mode is the value that appears most often. There are three 7's in the data set, and nothing else appears that often, so the mode is 7.

This could be a factor in pulling the mean a little higher since 7 is larger than 5, but even one large outlier (that is, a value far outside the range of the rest of the data set) could change the mean drastically.

Notice that mean and median rely on the data points having numerical values, so these measures of the center may only be used with quantitative data. On the other hand, the mode is the only measure of the center which can be used for qualitative data.

Measures of Dispersion

The measures of dispersion describe how to spread out a data set is. These are sometimes also called measures of variability or measures of spread. The simplest measure of dispersion is the range.

The range of a data set is the difference of the largest and smallest values in the data set, calculated with the simple formula max-min.

The standard deviation of a data set is defined as being the average distance from the mean of any data point in the set. It is calculated with this formula:

$$s = \frac{\sum(x - \overline{x})^2}{n-1} $$

(Note: This formula, in particular, is for calculating the standard deviation of a sample data set. To calculate the standard deviation of a population, use n rather than n-1 in the denominator.)

Central tendency describes the central point in a data set. Variability describes the spread of the data.
central tendency

Measures of Central Tendency

You are probably somewhat familiar with the mean, but did you know that it is a measure of central tendency? Measures of central tendency use a single value to describe the center of a data set. The mean, median, and mode are all the three measures of central tendency.

The mean, or average, is calculated by finding the sum of the study data and dividing it by the total number of data. The mode is the number that appears most frequently in the set of data.

The median is the middle value in a set of data. It is calculated by first listing the data in numerical order then locating the value in the middle of the list. When working with an odd set of data, the median is the middle number. For example, the median in a set of 9 data is the number in the fifth place. When working with an even set of data, you find the average of the two middle numbers. For example, in a data set of 10, you would find the average of the numbers in the fifth and sixth places.

The mean and median can only be used with numerical data. The mode can be used with both numerical and nominal data, or data in the form of names or labels. Eye color, gender, and hair color are all examples of nominal data. The mean is the preferred measure of central tendency since it considers all of the numbers in a data set; however, the mean is extremely sensitive to outliers, or extreme values that are much higher or lower than the rest of the values in a data set. The median is preferred in cases where there are outliers, since the median only considers the middle values.

Knowing what we know, let's calculate the mean, median, and mode using the example from before. Again, the anxiety ratings of your classmates are 8, 4, 9, 3, 5, 8, 6, 6, 7, 8, and 10.

Mean: (8+ 4 + 9 + 3 + 5 + 8 + 6 + 6 + 7 + 8 + 10) / 11 = 74 / 11 = The mean is 6.73.

Median : In a data set of 11, the median is the number in the sixth place. 3, 4, 5, 6, 6, 7, 8, 8, 8, 9, 10. The median is 7.

Mode: The number 8 appears more than any other number. The mode is 8.

Measures of Dispersion

We've got some pretty solid numbers on our data now, but let's say that you wanted to look at how spread out the study data are from a central value, i.e. the mean. In this case, you would look at measures of dispersion, which include the range, variance, and standard deviation.

The simplest measure of dispersion is the range. This tells us how spread out our data is. In order to calculate the range, you subtract the smallest number from the largest number. Just like the mean, the range is very sensitive to outliers.

The variance is a measure of the average distance that a set of data lies from its mean. The variance is not a stand-alone statistic. It is typically used in order to calculate other statistics, such as the standard deviation. The higher the variance, the more spread out your data are.

There are four steps to calculate the variance:

  1. Calculate the mean.
  2. Subtract the mean from each data value. This tells you how far each value lies from the mean.
  3. Square each of the values so that you now have all positive values, then find the sum of the squares.
  4. Divide the sum of the squares by the total number of data in the set.

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Video Transcript

What Are Descriptive Statistics?

Imagine that you are interested in measuring the level of anxiety of college students during finals week in one of your courses. You have 11 study participants rate their level of anxiety on a scale from 1 to 10, with 1 being 'no anxiety' and 10 being 'extremely anxious.' You collect the ratings and review them. The ratings are 8, 4, 9, 3, 5, 8, 6, 6, 7, 8, and 10. Your teacher asks you for a summary of your findings. How do you summarize this data? One way we could do this is by using descriptive statistics.

Descriptive statistics are used to describe or summarize data in ways that are meaningful and useful. For example, it would not be useful to know that all of the participants in our example wore blue shoes. However, it would be useful to know how spread out their anxiety ratings were. Descriptive statistics is at the heart of all quantitative analysis.

So how do we describe data? There are two ways: measures of central tendency and measures of variability, or dispersion.

Central tendency describes the central point in a data set. Variability describes the spread of the data.
central tendency

Measures of Central Tendency

You are probably somewhat familiar with the mean, but did you know that it is a measure of central tendency? Measures of central tendency use a single value to describe the center of a data set. The mean, median, and mode are all the three measures of central tendency.

The mean, or average, is calculated by finding the sum of the study data and dividing it by the total number of data. The mode is the number that appears most frequently in the set of data.

The median is the middle value in a set of data. It is calculated by first listing the data in numerical order then locating the value in the middle of the list. When working with an odd set of data, the median is the middle number. For example, the median in a set of 9 data is the number in the fifth place. When working with an even set of data, you find the average of the two middle numbers. For example, in a data set of 10, you would find the average of the numbers in the fifth and sixth places.

The mean and median can only be used with numerical data. The mode can be used with both numerical and nominal data, or data in the form of names or labels. Eye color, gender, and hair color are all examples of nominal data. The mean is the preferred measure of central tendency since it considers all of the numbers in a data set; however, the mean is extremely sensitive to outliers, or extreme values that are much higher or lower than the rest of the values in a data set. The median is preferred in cases where there are outliers, since the median only considers the middle values.

Knowing what we know, let's calculate the mean, median, and mode using the example from before. Again, the anxiety ratings of your classmates are 8, 4, 9, 3, 5, 8, 6, 6, 7, 8, and 10.

Mean: (8+ 4 + 9 + 3 + 5 + 8 + 6 + 6 + 7 + 8 + 10) / 11 = 74 / 11 = The mean is 6.73.

Median : In a data set of 11, the median is the number in the sixth place. 3, 4, 5, 6, 6, 7, 8, 8, 8, 9, 10. The median is 7.

Mode: The number 8 appears more than any other number. The mode is 8.

Measures of Dispersion

We've got some pretty solid numbers on our data now, but let's say that you wanted to look at how spread out the study data are from a central value, i.e. the mean. In this case, you would look at measures of dispersion, which include the range, variance, and standard deviation.

The simplest measure of dispersion is the range. This tells us how spread out our data is. In order to calculate the range, you subtract the smallest number from the largest number. Just like the mean, the range is very sensitive to outliers.

The variance is a measure of the average distance that a set of data lies from its mean. The variance is not a stand-alone statistic. It is typically used in order to calculate other statistics, such as the standard deviation. The higher the variance, the more spread out your data are.

There are four steps to calculate the variance:

  1. Calculate the mean.
  2. Subtract the mean from each data value. This tells you how far each value lies from the mean.
  3. Square each of the values so that you now have all positive values, then find the sum of the squares.
  4. Divide the sum of the squares by the total number of data in the set.

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Descriptive Statistics Thought Questions


Calculation Exercise

Suppose there are a group of 10 students taking a class at NoGo University. The students took a test and got the following grades: 65, 72, 78, 80, 81, 86, 89, 91, 92, 99. Calculate the descriptive statistic values of mean, median, and mode for this data.

Research Exercise

The lesson mentioned the measures of central tendency to be the mean, median, and mode. Search on the Internet to find other measures of central tendency. The lesson mentioned the measures of dispersion to be the range, variance, and standard deviation. Search again on the Internet to find other measures of dispersion. Which measures of central tendency are the most commonly used? Which measures of dispersion are most commonly used? Why are the other measurements of central tendency and of dispersion not commonly used? Write a report explaining your findings.

Calculation Exercise

The Mega Calorie Diet has been designed to help people gain weight. A group of five people on this diet gain 10 pounds, 15 pounds, 14 pounds, 8 pounds, and 13 pounds. The inventors of the diet want to know the measures of dispersion for this data, namely, range, variance, and standard deviation. Please calculate these values for them.

Discussion Exercise

Descriptive statistics are used frequently in quality assurance to describe a sample from a manufacturing process. Both the measures of central tendency and dispersion are monitored. Discuss what is happening with the items produced if the measures of central tendency are too high or too low. Then discuss what is happening with the items produced if the measures of dispersion are high or low. Is it possible for the measures of central tendency to be extreme, but the measures of dispersion to be fine? Is it possible for the measures of dispersion to be extreme, but the measures of central tendency to be fine?

What are the four types of descriptive statistics?

The four types of descriptive statistics are measures of frequency, measures of central tendency, measures of dispersion, and measures of position.

Measures of frequency include the count, frequency, and relative frequency. Measures of central tendency include the mean, median, and mode. Measures of dispersion include the range, standard deviation, and variance. Measures of position include percentile and quartile ranks.

What is an example of descriptive statistics in a research study?

Descriptive statistics examples in a research study include the mean, median, and mode. Studies also frequently cite measures of dispersion including the standard deviation, variance, and range. These values describe a data set just as it is, so it is called descriptive statistics.

What do you mean by descriptive statistics?

Descriptive statistics describe a data set as it is. In other words, descriptive statistics does not attempt to draw any conclusions for broader data sets or entire populations.

Measures of central tendency describe the value(s) around which a data set seems clustered, and measures of dispersion show how widespread the data is, based only on the information in the sample.

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