Finding the center point of your data set can be deceptively simple. There are a few different methods that you should be aware of when determining the central tendency, which come into play depending on the nature of your data.

Determining the center point of your data set can be helpful in a variety of ways. One example would be if you needed to know the average number of defects that occur each time there is a production run.

The center of the process is the central value in a set of data. Generally, the center is considered synonymous with the mean of a data set. The mean, however, is only one method of finding the central tendency. The best method for finding the center is dependent on the type of data you are working with.

There are some definite advantages to using the mean method to find the center point in a data set:

The center is the most common measure of central tendency that is utilized in various applications and is easily calculated.

Finding the center value in a data set is a good indicator of a typical value in the set.

A benefit of using the center is that this value takes all of the values in the data set into account.

Understanding the center of a data set is important because it can help in the identification of anomalies or outliers in the set.

Having a comprehension of the center of a data set is helpful because it offers a general overview of your data set, which allows you to have a fuller understanding of your data.

Understanding the center of a data set allows you to make a claim about the data set as a whole.

At a manufacturing plant, a purchasing manager is trying to determine if it is in the organization’s best interest to upgrade its machinery, since customers have become frustrated with the number of defects. In order to help with deciding on an upgrade, the purchasing manager decides to look at the number of defects that occur with the current machinery during a typical production run. In the last quarter, there were 30 defects in one month, 45 in another, and 20 in the last month of the quarter. Using the mean method to find the center, the purchasing manager finds that an average of 31.6 defects occurred during a typical month in the last quarter. The cost of addressing these defects looks to be greater than what it would be to simply upgrade the equipment at the plant. In order to be certain, the purchasing manager will look at how typical defects will be with an equipment upgrade.

Here are some best practices to consider when determining the center of your data set:

The mode is not an ideal method of finding the center in a data set, except in situations where you are working with categorical data. It is best used for finding the value that shows up the most frequently in a set, but not the actual center. In order to find the center, the most common methods are finding the mean or the median. Both of these options are good choices, but they might not line up exactly with one another. Generally speaking, to find the center point in a set of data, working with the mean is the most widely accepted method.

If you have a symmetrical distribution, using the mean to locate the center of your data set is generally accepted as the best method. However, if your data set is asymmetrical, working with the median is going to be the best option.

In order to find the center of your data set using the mean method, you simply add up your data values and then divide them by the number of observations. Using the median method, you order your data set from the smallest value to the largest. Next, you simply find the value that has an equal number of data points above and below it. In the case of data sets with an even number of values, you take the two center values and calculate the average of the two to find the median.

All methods used to find the central tendency use a single data point to try to describe a complete set of data.

If the data is continuous, then it should have all three. However, ordinal data only has a median and mode, while nominal data only has a mode.

It is possible for there to be more than one value using mode if more than one value occurs an equal number of times.

When determining the center point of your data, it is important to understand the data you are working with and how it will be applied. While the mean will work best in most instances, there are cases where the median or mode can give a clearer indication of the center.