Data Science Project Life-cycle
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Data preparation forms a large part of every data science project. Claims go to extremes, stating that 80-95% of the workload for data scientists consists of data preparation.

Outlier detection is one of the actions that make up this preparation phase. It is the process by which the analyst takes a closer look at the data and observes whether there are data points that behave differently. Such anomalies we call outliers and depending on the nature of the outlier the analyst might (not) want to handle them before continuing on to the modeling phase.

Outliers exist for several reasons, including:

  • The data may be incorrect.
  • The data may be missing but has not been registered as such.
  • The data may belong to a different sample.
  • The data may have (more) extreme underlying distributions (than expected).

Moreover, there are various types of outliers:

  • Point outliers are individual data points that are different from the rest of the dataset. This is the most common outlier in practice. An example would be a person of 2.10 meters tall in a dataset of sampled population lengths.
  • Contextual outliers are individual data points that would not necessarily be outliers based on their value but are because of the combination of their value with their current context. An example would be an outside temperature of 25 degrees Celcius, which is not necessarily weird but is most definitely unusual during the December month.
  • Collective outliers are collections of data points that are collective different from the rest of the data sample. Again, the individual data points would not necessarily be outliers based on their individual value but are because of their values combined. An example would be a prolonged period of extreme drought. Where days without rain may not be outliers necessarily, a long stretch without precipitation can be considered an anomaly.

There is no rigid definition of what makes a data point an outlier. One could even state that determining whether or not a data point is an outlier is a quite subjective exercise. Nevertheless, there are multiple approaches and best practices to detecting (potential) outliers.

  • Univariate outliers: When a case or data point has an extreme value on a single variable, we refer to it as a univariate outlier. Standardized values (Z-scores) are a frequently used method to detect univariate outliers on continuous variables. However, here the researcher will have to determine a certain threshold. For example, (-)3.29 is frequently used, where data points whose Z-value lies beyond this value are considered outliers. Here, chances would be 0.005%, or 1 in 2000, of obtaining this value if the variable follows a normal distribution. As you can see, the larger the dataset, the more likely you are to find such extreme values.
  • Bi- & multivariate outliers: A combination of unusual values on multiple variables simultaneously is referred to as a multivariate outlier. Here, a bivariate outlier is an outlier based on two variables. Normally you’d first check and handle univariate outliers, before turning to bi- or multivariate outliers. The process here is somewhat more complicated than for univariate outliers, and there are multiple approaches one can take (e.g. distance, leverage, discrepancy, influence). For example, you can look at the distance of each data point in the multivariate space (X1 to Xp) compared to the other data points in that space. If the distance is larger than a certain threshold, the data point can be considered a multivariate outlier, as it is that much different from the rest of the data considering multiple variables simultaneously.
  • Visualization: In trying to detect univariate outliers, data visualizations may come in handy. For example, histograms or frequency distributions will quickly demonstrate any data point that has unusually high or low values. Boxplots can similarly hint at values that fall only just outside of the expected range or are really extreme outliers. Boxplots combine visualization with model-based detected.
  • Model-based: Apart from the above-mentioned standardization with Z-values, there are multiple model-based methods for outlier detection. Most assume that the data follows a normal or Gaussian distribution, and hence identify which data points are unlikely based on the data’s mean and standard deviation. Examples are Dixon’s Q-test, Tukey’s test, Thompson Tau test, and Grubb’s test.
  • Grouped data: If there is a grouping variable involved in the analysis (e.g., logistic regression, analyses of variance) then the data of each group can best be assessed for outliers separately. What can be considered an outlier in one group is not necessarily an unusual observation in a different group. If the analysis to be performed does not contain a grouping variable (e.g., linear regression, SEM), then the complete dataset can be assessed for outliers as a whole.

There are several ways to handle outliers:

  • Keep them as is.
  • Exclude the data point (i.e., censoring/trimming/truncating).
  • Replace the data point with a missing value.
  • Replace the data point by the nearest ‘regular’ value (i.e., Winsoring).
  • Run models both with and without outliers.
  • Run model corrections within the analysis (only possible in specific models)

There are several reasons why you may not want to deal with outliers: 

  • When taking a large sample, outliers are part of what one would expect.
  • Outliers may be part of the explanation for the phenomena under investigation
  • Several machine learning and modeling techniques are robust to outliers or may be able to correct for them.

To end on a light note, Malcolm Gladwell wrote a wonderful book called Outliers. In it, he examines the factors for personal success; the reasons why “outliers” such as Bill Gates have become so stinkingly wealthy. He goes to show that these successful people are not necessarily random anomalies or outliers, but that there are perfectly sensible explanations for their success.

References and further reading:

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