Outliers are data points that are significantly different from the rest of the data and can skew the results of the analysis. There are several methods used to detect outliers, including:
-
Z-score: This method involves calculating the Z-score for each data point, which measures how many standard deviations the data point is from the mean. Data points with a Z-score above a certain threshold, such as 3 or 4, are considered outliers.
-
Modified Z-score: This method is similar to the Z-score method but uses the median and median absolute deviation (MAD) instead of the mean and standard deviation. Data points with a modified Z-score above a certain threshold, such as 3 or 4, are considered outliers.
-
Box plots: A box plot is a graphical representation of the data that shows the quartiles, median, and outliers. Data points outside the whiskers, which extend to 1.5 times the interquartile range (IQR) above or below the box, are considered outliers.
-
Cook's distance: Cook's distance is a measure of the influence of a data point on the regression model. Data points with a Cook's distance above a certain threshold, such as 0.5 or 1, are considered influential and may be outliers.
-
Mahalanobis distance: The Mahalanobis distance measures the distance between a data point and the mean of the data, taking into account the correlation among the variables. Data points with a Mahalanobis distance above a certain threshold, such as 3 or 4, are considered outliers.
If you want to learn more about Data Validation check out our Data Analytics Course video on YouTube. Our course covers everything you need to know about these types of analytics and how to effectively use them to drive informed decision-making.
The choice of outlier detection method depends on the type of data being analyzed and the research question being addressed. It is often useful to use multiple methods to detect outliers and compare the results to identify potential data quality issues.
