Three Sigma Limits Statistical Calculation With Example

Will Kenton is an expert on the economy and investing laws and regulations. He previously held senior editorial roles at Investopedia and Kapitall Wire and holds a MA in Economics from The New School for Social Research and Doctor of Philosophy in English literature from NYU.

Updated July 31, 2024 Reviewed by Reviewed by Khadija Khartit

Khadija Khartit is a strategy, investment, and funding expert, and an educator of fintech and strategic finance in top universities. She has been an investor, entrepreneur, and advisor for more than 25 years. She is a FINRA Series 7, 63, and 66 license holder.

Three Sigma Limits

What Is a Three Sigma Limit?

A three sigma limit is a statistical calculation in which the data are within three standard deviations from a mean. Three sigma refers to processes in business applications that operate efficiently and produce items of the highest quality.

Three sigma limits are used to set the upper and lower control limits in statistical quality control charts. Control charts establish limits for a manufacturing or business process that's in a state of statistical control.

Key Takeaways:

Understanding Three Sigma Limits

Control charts are also known as Shewhart charts, named after Walter A. Shewhart, an American physicist, engineer, and statistician (1891–1967). Control charts are based on the theory that a certain amount of variability in output measurements is inherent even in perfectly designed processes.

Control charts determine if there's a controlled or uncontrolled variation in a process. Variations in process quality due to random causes are said to be in control. Out-of-control processes include both random and special causes of variation. Control charts are intended to determine the presence of special causes.

Statisticians and analysts use a metric known as the standard deviation to measure variations, also referred to as sigma. It's a statistical measurement of variability showing how much variation exists from a statistical average.

Sigma measures how far observed data deviates from the mean or average. Investors use standard deviation to gauge expected volatility.

Consider the normal bell curve which has a normal distribution. The farther to the right or left a data point is recorded, the higher or lower the data is than the mean. Low values indicate that the data points fall close to the mean. High values indicate that the data is widespread and not close to the average.

Example of Calculation

Let’s consider a manufacturing firm that runs a series of 10 tests to determine whether there's a variation in the quality of its products. The data points for the 10 tests are 8.4, 8.5, 9.1, 9.3, 9.4, 9.5, 9.7, 9.7, 9.9, and 9.9.

  1. First, calculate the mean of the observed data: (8.4 + 8.5 + 9.1 + 9.3 + 9.4 + 9.5 + 9.7 + 9.7 + 9.9 + 9.9) / 10, which equals 93.4 / 10 = 9.34.
  2. Second, calculate the variance of the set: Variance is the spread between data points and is calculated as the sum of the squares of the difference between each data point and the mean divided by the number of observations. The first difference square will be calculated as (8.4 - 9.34) 2 = 0.8836, the second square of difference will be (8.5 - 9.34) 2 = 0.7056, the third square can be calculated as (9.1 - 9.34) 2 = 0.0576, and so on. The sum of the squares of all 10 data points is 2.564. The variance is therefore 2.564 / 10 = 0.2564.
  3. Third, calculate the standard deviation: This is simply the square root of the variance. The standard deviation = √0.2564 = 0.5064.
  4. Fourth, calculate three sigma: This is three standard deviations above the mean. It's (3 x 0.5064) + 9.34 = 10.9 in numerical format. None of the data is at such a high point so the manufacturing testing process has not yet reached three sigma quality levels.

How Are Three Sigma Limits Used?

Three sigma limits set a range for the process parameter at 0.27% control limits. Three sigma control limits are used to check data from a process and to determine if it's within statistical control by checking if data points are within three standard deviations from the mean. The upper control limit (UCL) is set three sigma levels above the mean and the lower control limit (LCL) is set at three sigma levels below the mean.

What Is Standard Deviation?

Standard deviation is a statistical measurement. It calculates the spread of a set of values against their average. It's the positive square root of the variance and defines the difference between the variation and the mean.

What Is a Bell Curve?

A bell curve gets its name from its appearance: a bell-shaped curve that rises in the middle. It illustrates normal probability and several graphs and distributions use it. The single line measures data on one, two, and three standard deviations.

The Bottom Line

The term "three sigma" points to three standard deviations. Shewhart set three standard deviation (3-sigma) limits as a rational and economic guide to minimum economic loss.

Around 99.73% of a controlled process will occur within plus or minus three sigmas so the data from a process ought to approximate a general distribution around the mean and within the predefined limits. Data that lie above the average and beyond the three sigma line on a bell curve represent less than 1% of all data points.

Article Sources
  1. National Center for Biotechnology Information. "Walter A. Shewhart, 1924, and the Hawthorne Factory."
  2. FasterCapital. "Defect Rate: Reducing Defect Rates Using Three Sigma Limits."
  3. Britannica. "Standard Deviation."
  4. CFI Education. "Bell Curve."
Open a New Bank Account Advertiser Disclosure

The offers that appear in this table are from partnerships from which Investopedia receives compensation. This compensation may impact how and where listings appear. Investopedia does not include all offers available in the marketplace.

Related Terms

Joint probability is a statistical measure that calculates the likelihood of two events occurring together and at the same point in time.

The Durbin Watson statistic is a number that tests for autocorrelation in the residuals from a statistical regression analysis.

A simple random sample is a subset of a statistical population where each member of the population is equally likely to be chosen. Learn more about statistical sampling.

Correlation is a statistical measure of how two securities move in relation to each other. Investors use correlation to diversify their portfolios and hedge against risk.

The least squares method is a statistical technique to determine the line of best fit for a model, specified by an equation with certain parameters to observed data.

Growth rates are the percent change of a variable over time. It can be applied to GDP, corporate revenue, or an investment portfolio. Here’s how to calculate growth rates.

Related Articles

Joint Probability

Joint Probability: Definition, Formula, and Example

How To Calculate VaR: Finding Value at Risk in Excel

Durbin Watson Test

Durbin Watson Test: What It Is in Statistics, with Examples

Simple Random Sample

Simple Random Sampling: 6 Basic Steps With Examples

Correlation

Correlation: What It Means in Finance and the Formula for Calculating It

Least Squares Methods

Least Squares Method: What It Means, How to Use It, With Examples Partner Links Investopedia is part of the Dotdash Meredith publishing family.

We Care About Your Privacy

We and our 100 partners store and/or access information on a device, such as unique IDs in cookies to process personal data. You may accept or manage your choices by clicking below, including your right to object where legitimate interest is used, or at any time in the privacy policy page. These choices will be signaled to our partners and will not affect browsing data.

We and our partners process data to provide:

Store and/or access information on a device. Use limited data to select advertising. Create profiles for personalised advertising. Use profiles to select personalised advertising. Create profiles to personalise content. Use profiles to select personalised content. Measure advertising performance. Measure content performance. Understand audiences through statistics or combinations of data from different sources. Develop and improve services. Use limited data to select content. List of Partners (vendors)