All types of data frequently contain outliers, and to ensure the accuracy and greater significance of your study, it’s critical to recognize and handle these outliers.
What exactly are outliers, and why is it crucial to locate them?
A data point that significantly exceeds the other data points in the data set is called an outlier. When you find an outlier in your data, it might skew your results and cause false conclusions to be drawn.
Outliers in any direction are possible when working with real datasets in Excel (i.e., a positive or a negative outlier), and it is essential for you to know how to remove outliers in Excel. You must find a way to recognize these outliers and determine the appropriate course of action for them to ensure the accuracy of your study.
To Identify Outliers, Sort The Data
One easy technique to find outliers in small datasets is to manually go over some of the numbers at the top of the sorted data after it has been sorted.
Additionally, sort the data first in ascending order, then in descending order, and finally, go through the highest values, as there may be outliers in both directions.
Small datasets that are suitable for manual data scanning can be used with this technique. Although not scientific, this approach is effective.
Utilizing the Quartile Functions to Identify Outliers
Let’s now discuss a more systematic approach that can assist you in determining whether or not there are any outliers.
A quartile is one-fourth of the data set in statistics. In the event that you have twelve data points, for instance, the first quartile would be the bottom three, the second quartile would be the next three, and so on.
The data collection I’m looking for outliers in is below. To accomplish this, I must compute the first and third quartiles and then use the results to determine the upper and lower bound.
Now that you know the upper and lower bounds of the data set, you may rapidly locate the values that do not fall inside this range by returning to the original data.
To accomplish this quickly, verify each value and output a TRUE or FALSE result in a new column. The Outlier column can now be filtered only to display records with a value of TRUE. Alternatively, you can highlight every cell in which the value is TRUE by using conditional formatting.
Locating the Exceptions Employing the SMALL/LARGE Functions
You can extract the highest and lowest 5 or 7 values from a huge data set (values in numerous columns) to check for any outliers. Without having to go through all of the data in both ways, you can spot any outliers.
Here are two strategies you may employ to deal with outliers and ensure accuracy in your data analysis.
Eliminate The Anomalies
Eliminating outliers from your data set is as simple as just deleting them. In this method, your analysis won’t be tainted.
When you have huge datasets and removing a few outliers won’t affect the analysis as a whole, it’s a more practical method. Naturally, please make a copy of the data and investigate the cause of these outliers before erasing it.
Adjust The Value To Normalize The Outliers
When I worked a full-time job, I used to normalize the outliers. I would just adjust all of the outlier values to be somewhat higher than the data set’s maximum value.
This ensured that there was no discard of the data while also preventing it from distorting my findings.
As an actual example, if you were examining the net profit margins of businesses and found that most were between the range of -10% and 30%, with a few percentages exceeding 100%, I would simply adjust these outlier values to 30% or 35%.
To ensure you have the right data set and can derive the right reports and insights, ensure the removal of outliers in Excel and make better decisions.
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