The effect of outliers on data validity

The effect of outliers on data validity

The Effect of Outliers on Data Validity

When it comes to analyzing and interpreting data, outliers can easily skew the results, making the conclusions drawn from the data invalid. Outliers are data points that are significantly different from the other data points in a dataset. However, determining whether or not an outlier should be removed can be a complex decision, and it’s important to understand the potential impact on the validity of the data.

To start, it’s important to understand how outliers can impact statistics. Outliers can significantly impact summary statistics such as the mean and standard deviation. These statistics are measures of central tendency and dispersion, respectively. When outliers are present in a dataset, the mean is shifted towards the outlier, and the standard deviation is inflated. This can make the dataset appear more spread out than it actually is.

Additionally, outliers can impact inferential statistics, such as hypothesis testing and confidence intervals. These statistics are used to draw conclusions about a population based on a sample. However, outliers can impact the accuracy of these statistics, making the conclusions drawn from the sample invalid.

So, when should outliers be removed from a dataset? There is no straightforward answer to this question, as it depends on the context of the data and the goals of the analysis. Sometimes outliers are a result of measurement error and should be removed. However, other times they are a legitimate part of the data and removing them would undermine the validity of the analysis.

One approach to determining whether or not to remove outliers is to use graphical methods to identify outliers and understand their impact on the data. Box plots and scatter plots are commonly used to visually display outliers. These plots allow you to see the distribution of the data and identify any extreme values.

Another approach is to use statistical tests to evaluate the impact of outliers. For example, the Grubbs’ test is a statistical test that can detect an outlier in a univariate dataset. This test calculates a statistic known as the G-statistic, and if the calculated G-statistic is greater than a critical value, the data point can be considered an outlier.

Regardless of the approach used, it’s important to document any decisions made regarding outlier removal and explain why the decision was made. Removing outliers without valid justification can be seen as manipulating the data and undermines the integrity of the analysis.

In addition to potential impacts on statistics, outliers can also impact machine learning algorithms. These algorithms are used to identify patterns in data and make predictions about future data. However, if outliers are present in the training data, the machine learning algorithm may be less accurate in predicting future data. Therefore, it’s important to carefully consider whether or not to remove outliers in order to ensure the accuracy of machine learning algorithms.

In conclusion, outliers can have a significant impact on the validity of data analysis and machine learning algorithms. However, it’s important to carefully consider whether or not to remove outliers, as their presence may be a legitimate part of the data. Any decisions made regarding outlier removal should be thoroughly documented and justified. By understanding the potential impact of outliers on data validity, analysts and data scientists can make informed decisions about how to handle them.


Garcia, L., Gamez, M., & Batista, G. (2020). An empirical comparison of techniques for handling outliers in machine learning classification tasks. Expert systems with applications, 159, 113497.

Goodall, C. R. (2011). Outlier detection: Some concepts, Computers & Chemical Engineering, 35(3), 571-580.

Grubbs, F. E. (1950). Sample criteria for testing outlying observations. The Annals of Mathematical Statistics, 21(1), 27-58.