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DEEPCHECKS GLOSSARY

Handling Outliers

We frequently utilize data to form inferences and draw conclusions to make fact-based judgments. For data analysis, there are several strong statistical tools at your disposal. Some of these technologies, on the other hand, are extremely sensitive to the existence of outliers in the data. We may draw incorrect conclusions if we disregard outliers or apply incorrect statistical methods for analysis. As a result, we must learn how to deal with outliers. What are outliers, first and foremost? Outliers are extraordinary values that deviate significantly from the rest of our data set’s observations. Looking at the outlier, it appears that this data does not belong with the rest of the data set since it appears to be distinct.

What should you do?

It’s sometimes preferable to leave outliers in your data. They can acquire important data that is relevant to your research. It’s difficult to keep these points, especially when they lose statistical significance! However, omitting extreme values purely because of their extremeness might skew the results by obliterating information about the research area’s intrinsic variability. You’re making the subject look less changeable than it is.

When deciding whether or not to delete an outlier, assess if it accurately represents your target demographic, topic area, research question, and research methods. Was there anything unexpected about the measurements, such as power outages, strange experimental circumstances, or anything else out of the ordinary? Is there anything that distinguishes an observation, whether it’s of a person, an object, or a transaction? Were there any problems in measurement or data entry?

If you’re looking for an outlier, consider the following:

If there is a measurement or data input error, attempt to fix it as soon as feasible. Remove that observation if you can’t fix it since you know it’s inaccurate. You can lawfully eliminate the outlier if it is not a member of the population you are researching.

You should not eliminate it since it is a natural component of the population you are investigating.

When you decide to eliminate outliers, make a list of the data points that were left out and explain why. When deleting outliers, you must be able to pinpoint a specific cause. Another option is to run the analysis both with and without the observations and compare the results. This method of comparing findings is especially beneficial when you’re unclear whether or not to remove an outlier and when there’s a lot of dispute within a group regarding the answer.

Statistical Analyses

What do you do when you can’t legally eliminate outliers yet contradict your statistical analyses’ assumptions? You want them to be included, but you don’t want them to skew the findings. Here are a few choices to consider.

Outliers are not a problem for nonparametric hypothesis testing. Outliers will not necessarily violate assumptions or skew results in these alternatives to the more usual parametric tests.

You can try converting your data or utilizing a robust regression analysis provided in various statistical software when doing regression analysis.

Finally, bootstrapping approaches do not make assumptions about distributions and use the sample data as is.

These sorts of studies allow you to capture all of your dataset’s variability without breaking any assumptions or skewing the results.

Recap

Outliers are extreme deviating numbers in data that might produce discrepancies in results and affect the results of our study. Outliers in data collection can be caused by a variety of factors, including sampling and measurement mistakes. Before we can deal with outliers, we must first recognize them, which may be done using methods like box plots, scatter plots, and histograms. Outliers should not be eliminated from our study since they might provide significant information about our procedures in some circumstances. There are many ways to deal with outliers in data, and there is no one-size-fits-all solution. In most situations, human skill and experience are used to determine how to effectively deal with outliers in our data.

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