Statistics. Although others may stifle a yawn at the mere mention, Six Sigma practitioners who have completed the Lean Six Sigma Green Belt course know that Six Sigma projects will not succeed without statistics. Statistics are just part of the Six Sigma data-driven Six Sigma approach. We need to be able to understand the basics of many statistical methods in order to reach the Six Sigma Green Belt level. Six Sigma Black Belts, Green Belts, and Green Belts use MINITAB to do calculations. It is important to understand the fundamental principles behind a measure, such the measures of central tendence.
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As you will learn in a Lean Six Sigma course, statistical tools are used during the Measure and Analyze phases. Measures of central tendency are the most basic statistics that Six Sigma practitioners will be familiar with. Six Sigma teams will look at measures of central tendency as soon as they have collected data.
There are two branches to statistics
Let’s first take a look at two types of statistics. The first branch is called Descriptive Statistics, while the second is called Inferential Statistics. Descriptive statistics can be used to describe a population or process. Descriptive statistics can also include measures of central tendency. Inferential statistics are based on a sample from the population to make inferences or predictions about a population. These are used in the Analyze phase (DMAIC process).
Measures of central tendencies
What are measures of central tendencies? The measure of central tendency is a single value that attempts describe a set by identifying its central position within the data. Measures of central tendency are also known as measures of central position. It is a measure that shows us where the middle of a collection of data is. The most well-known measure of central tendency is the mean, also known as the average. There are also other measures of central tendencies, such as the median or the mode. All measures of central tendencies are valid, including the median, mode, and mean.
Measures of Central Tendency
The most well-known and most widely used measure of central tendency is the mean. Although it can be used with either discrete or continuous data, it is more commonly used with continuous data. The sum of all values in a data set divided by its number is the mean. The formula for the mean is: sum of all observations divided by number
Measures of Central Tendency: How do you Calculate the Mean?
This article will provide examples that illustrate each measure of central tendency. Let’s take a look at the figure below to see some examples of the mean. The numbers 1-8 have been added together and the sum divided by 8. The answer is 4.5. The average or mean value is 4.5

In the second example, the wait time at the hospital was affecting the turnaround time for basic blood analyses. To calculate the average turnaround times, data was collected for 10 tests on a given date. Data collection was measured in minutes. To calculate the average turnaround time for basic analysis, we added up the turnaround times for ten blood analyses tests. This yields 61.3 minutes. This is the most famous measure of central tendency.
Median Measures of Central Tendency
These are the measures of central tendencies