What Outlier is
An outlier is an observation in a dataset that is numerically distant from the rest of the data. Outliers can have a significant effect on statistical analyses and can lead to inaccurate results. The most common steps for identifying outliers in a dataset are:
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Visual Inspection: A simple way to detect outliers is to create a graphical representation of the data such as a boxplot or a histogram. Outliers are typically the data points that are far away from the majority of the other data points.
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Calculation of Statistical Measures: Outliers can also be identified by calculating the mean, median, and standard deviation of the dataset. An outlier is an observation that is more than 1.5 times the interquartile range (IQR) away from either the first or third quartile.
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Use of Outlier Detection Algorithms: Outlier detection algorithms such as k-nearest neighbors (KNN) and local outlier factor (LOF) can also be used to identify outliers in a dataset. These algorithms look at the data points in the dataset and compare them to each other to identify unusual data points.
Once outliers have been identified, they can be removed from the dataset or the analysis can be adjusted to account for their presence. It is important to note that outliers should not be removed without good reason as they may provide valuable insights into the data.
Examples
- A data point that lies significantly further away from the rest of the data points in a distribution.
- Data points that are more than three standard deviations away from the mean.
- Measurement errors caused by faulty instruments or data entry mistakes.
- Unusual spikes in a time series data.
- A data point that has a much higher or lower value than the rest of the data points in a sample.