Handling Outliers

Introduction to Outliers

Frequently, we rely on data to draw inferences and solid conclusions for informed decision-making. A host of robust statistical tools exist to facilitate data analysis. However, these tools are often quite sensitive to outliers in the represented data.

Defining Outliers

Outliers are unusual values that markedly deviate from other observations in our data set. These values seem out of place and distinct but could hold critical information relevant to the study.

Sometimes, it's more beneficial not to exclude these outliers from your data. While retaining them may often compromise statistical significance, dropping these values based solely on their extremeness can bias the results by eliminating inherent variability.

Deciding whether to exclude an outlier should heavily depend on:

  • Its congruity with the subject of your analysis
  • Research query and employed methodologies
  • Unexpected circumstances during the measurements
  • Errors in measurement or data recording

If you encounter an outlier during your research, aim to rectify the data or input error as promptly as possible. If you cannot correct it, and you're certain about its inaccuracy, it's reasonable to exclude it. However, if it's a natural part of the population you're studying, it should not be discarded.

Best Practices in Handling Outliers

  • Documentation: Upon deciding to eliminate outliers, ensure you adequately document the excluded data points and articulate your reasons.
  • Comparison: Consider performing the analysis both with and without the outliers and draw comparisons.
  • Alternative Statistical Methods: Nonparametric statistical tests, for instance, are not affected by outliers. Consider transforming your data or utilize robust regression analysis. Besides, bootstrapping methods use sample data as is, capturing all variability without violating presumptions.


Outliers are exceptionally deviating numbers in a data set that can engender discrepancies and impact the study's results. Identifying these outliers using techniques like box plots, scatter plots, and histograms is crucial. They offer potentially significant insights about the research process and should not be excluded rashly. There is no one-size-fits-all approach in handling outliers in data, and often, it comes down to expertise and experience to decide how to navigate them effectively.

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