Standard Deviation: A Comprehensive Overview

May 3, 2023
Have you ever wondered how statisticians and researchers measure variability in data? One commonly used metric for this is the standard deviation. Standard deviation is a statistical concept that can be a bit tricky to grasp at first, but it's also one of the most widely used tools in data analysis. In simple terms, Standard deviation measures the amount of variability or spread in a set of data. By the end of this article, you will have a solid understanding of standard deviation and its relevance in the world of statistics. Read all the way through since you might need to use standard deviation in your life at some point.

What is Standard Deviation?

Statistically, the standard deviation measures how much a set of data varies or its dispersion. Standard deviation is a widely used tool in data analysis that helps researchers and statisticians understand the distribution of the data and how the data points are spread out around the mean or average value. The standard deviation is represented by the Greek letter sigma (σ) for a population and s for a sample.

The formula for calculating standard deviation is:

σ = √(Σ(x-μ)²/N)


σ = standard deviation

Σ = the summation of all dataset values

x = each value in the dataset

μ = the mean or average of the dataset

N = the dataset's total values

To further comprehend the formula, let's examine each of its components:

(x - μ) - This represents the difference between each data point and the mean. It measures how far away each value is from the average.

(x - μ)² - Squaring the differences between each value and the mean gives us the squared deviation from the mean. This is done to avoid negative values in the calculation and to give more weight to larger deviations from the mean.

Σ(x - μ)² - This represents the sum of the squared deviations from the mean for all the values in the dataset.

N - This represents the total number of values in the dataset.

√(Σ(x - μ)²/N) - The square root of the sum of squared deviations divided by the total number of values gives us the standard deviation.

In other words, the standard deviation is the square root of the average squared deviation of each data point from the mean. It tells us how much the data deviates from the average and how to spread out the data points around the mean value.

An Example of How to Calculate the Standard Deviation

Step 1: Calculate the Mean

To calculate the standard deviation, we first need to calculate the mean or average of the dataset.

μ = (5 + 10 + 15 + 20 + 25) / 5

μ = 15

The mean or average of the dataset is 15.

Step 2: Calculating the difference between the data points and the mean

For each of the data values, we need to calculate their difference from the mean

(x - μ) = (5 - 15), (10 - 15), (15 - 15), (20 - 15), (25 - 15)

(x - μ) = -10, -5, 0, 5, 10

Step 3: Square the Differences

Now we square each difference we calculated in step 2.

(x - μ)² = (-10)², (-5)², 0², 5², 10²

(x - μ)² = 100, 25, 0, 25, 100

Step 4: Sum the Squared Differences

Add up the squared differences we calculated in step 3.

Σ(x - μ)² = 100 + 25 + 0 + 25 + 100

Σ(x - μ)² = 250

Step 5: Divide by the Number of Values in the Dataset

Now divide the sum of squared differences by the total number of values in the dataset.

N = 5

Σ(x - μ)²/N = 250/5

Σ(x - μ)²/N = 50

Step 6: Take the Square Root

To obtain the standard deviation, take the square root of the value from step 5.

σ = √50

σ ≈ 7.07

Therefore, the standard deviation of the dataset {5, 10, 15, 20, 25} is approximately 7.07.

This tells us that the data points are spread out around the mean value of 15, with some values being farther away from the mean than others. The standard deviation gives us a measure of the amount of variability in the dataset.

Using Statistical Software to Calculate Standard Deviation

Calculating standard deviation can be a time-consuming process, especially for large datasets. Fortunately, statistical software can perform the calculation quickly and accurately.

There are many statistical software options available, including both free and paid versions. Some popular options include Excel, R, SPSS, and SAS.

Using statistical software to calculate standard deviation typically involves the following steps:

  1. Enter the dataset into the software.
  2. Depending on whether you have data for a population or a sample, choose the correct formula for calculating standard deviation.
  3. Run the calculation.
  4. Review the results, including the mean and standard deviation.

Let's take a closer look at how to calculate standard deviation using Excel as an example:

  1. Enter the dataset into Excel, with each value in a separate cell in a column.
  2. Select an empty cell where you want to display the standard deviation.
  3. Enter the formula "=STDEV (range)" into the cell, replacing "range" with the range of cells containing your data. For example, if your data is in cells A1 to A10, the formula would be "=STDEV (A1:A10)".
  4. Press enter, and Excel will calculate and display the standard deviation.

Using statistical software to calculate standard deviation not only saves time but also reduces the risk of human error. Additionally, most statistical software packages provide additional features and tools for analyzing and interpreting data, making them valuable resources for researchers and analysts.

Types of Standard Deviation

Standard deviations can be divided into two categories: sample standard deviation and population standard deviation.

When we have information for a sample of a population, we use the sample standard deviation. Since we lack information for the complete population in this instance, we must estimate the population's parameters using the data from the sample. The denominator of the formula for sample standard deviation is N-1 (the total number of values minus one) rather than N, which is a significant distinction from the formula for population standard deviation (the total number of values). This is accomplished to account for the fact that the population standard deviation frequently exceeds the sample standard deviation.

The population standard deviation, on the other hand, is used when we have data for a complete population. In other words, there is no need to estimate or draw conclusions about the population because we have data for every person or thing in the population. The denominator of the formula for population standard deviation is N (the total number of values in the population) rather than N-1, which is a significant distinction from the formula for sample standard deviation (the total number of values minus one, which is used in sample standard deviation).

Depending on whether you have data for a population or a sample, it is crucial to employ the correct formula to calculate the standard deviation. The erroneous use of a formula might result in false results and estimates of the data's variability.

How to Interpret Standard Deviation

Standard deviation is a statistical measure that provides information about the spread or variability of a dataset. In general, the higher the standard deviation, the more spread out the data is. Conversely, the lower the standard deviation, the more tightly clustered the data is around the mean.

To understand the interpretation of standard deviation, let's consider a few examples:

Example 1: Exam Scores

Suppose we have the exam scores of ten students in a class: 60, 65, 70, 75, 80, 85, 90, 95, 100, and 100. The mean score is 82, and the standard deviation is 15.

The high standard deviation in this example suggests that the scores are widely spread out. There is a significant range of scores, with some students performing very well and others performing poorly. The standard deviation of 15 indicates that most students scored within 15 points of the mean score of 82.

Example 2: Heights of Plants

Suppose we have a dataset that represents the heights of ten plants in a garden: 10cm, 12cm, 11cm, 9cm, 10cm, 11cm, 12cm, 10cm, 11cm, and 9cm. The mean height is 10.5cm, and the standard deviation is 1.07cm.

In this example, the low standard deviation indicates that the heights of the plants are very tightly clustered around the mean height of 10.5cm. This means that the variation in the heights of the plants is not very significant, and they are all quite similar in height.

Example 3: Sales Figures

Suppose we have a dataset that represents the sales figures of ten sales representatives in a company: $100,000, $110,000, $120,000, $130,000, $140,000, $150,000, $160,000, $170,000, $180,000, $190,000. The mean sales figure is $150,000, and the standard deviation is $35,355.

In this example, the high standard deviation suggests that there is a significant range in sales figures among the sales representatives. Some sales representatives are performing very well and exceeding the average sales figure, while others are performing poorly and falling below the average sales figure. The standard deviation of $35,355 indicates that most sales representatives' sales figures are within $35,355 of the mean sales figure of $150,000.

Thus far, it is clear that interpreting the standard deviation provides valuable information about the spread or variability of a dataset. Understanding the range of the data and the degree of variability is essential for drawing accurate conclusions and making informed decisions based on the data.

Standard Deviation Uses

Standard deviation is a versatile statistical tool with many uses across various fields. Some of the most common uses of standard deviation include the following.

Standard deviation is frequently used in quality control to determine whether a product or process is consistent. By measuring the standard deviation of a set of data, manufacturers can ensure that their products meet the desired quality standards and identify any issues that need to be addressed.

In finance, the standard deviation is used to measure the volatility or risk associated with an investment. A higher standard deviation indicates that the investment's returns are more volatile and unpredictable, making it riskier than an investment with a lower standard deviation.

Standard deviation is often used in medical research to measure the variability of certain medical data such as blood pressure or cholesterol levels. This helps researchers identify patterns and potential health risks in large groups of people.

Standard deviation is used in educational assessment to measure the consistency of test scores. A high standard deviation suggests that the test was difficult for some students, while a low standard deviation indicates that the test was consistent and straightforward.

A standard deviation is a crucial tool for data analysis, helping analysts to understand the distribution of data and identify outliers. By measuring the standard deviation, analysts can determine the spread of data around the mean and decide whether the data set is normal or skewed.

Standard Deviation Limitations

Although a frequently used statistical tool, standard deviation has some drawbacks. The following are some of the biggest shortcomings of standard deviation:

For instance, the standard deviation is sensitive to extreme values that deviate greatly from the majority of the data points, called outliers. The standard deviation can be significantly impacted by these outliers, which makes it less accurate as a measure of variability.

Standard deviation also makes the assumption that the data is evenly distributed. However, the standard deviation might not adequately reflect the variability of the data if it is skewed or not evenly distributed.

The size of the sample affects how accurate the standard deviation is. Larger sample sizes can provide smaller standard deviations, whereas smaller standard deviations can be produced by fewer sample numbers.

Scale dependence might also limit the effectiveness of the standard deviation measure. The size of the data affects the standard deviation. The standard deviation can be dramatically affected by changing the scale of the data, such as going from centimeters to inches.

Finally, data with more than one peak or that are multimodal are more difficult to quantify using the standard deviation. Other statistical techniques, such as range or interquartile range, maybe more applicable in such circumstances.


Standard deviation is a valuable statistical tool that provides insights into the variability of data, making it useful in a variety of applications, including quality control, finance, medical research, and education. However, it is important to recognize that standard deviation has its limitations, such as sensitivity to outliers, skewed data, sample size, scale dependence, and multimodal data. To fully comprehend the data, analysts must be aware of these restrictions and combine standard deviation with additional statistical tools. By doing so, they can then make informed decisions and draw meaningful conclusions from their data analysis.