Outliers refer to data points that differ significantly from other observations in a dataset. They can appear either because of variability in the data or due to anomalies or errors during data collection.
An outlier can skew interpretations and conclusions about the data and can potentially be misleading, so it’s often important to investigate and manage outliers appropriately.
There are different types of outliers:
Univariate Outliers: These are data anomalies in one variable. They can be easily identified using statistical methods, such as z-scores or visualization methods like box plots and histograms.
Multivariate Outliers: These are anomaly combinations of scores on two or more variables. Scatter plots and multidimensional scaling can help identify them.
Time Series Outliers: These are unexpected data points in a time series. For instance, sudden spikes or drops can be outliers when the general trend of the data points in one direction.
Understanding the reasons behind outliers is essential:
Data Entry Errors: Manual errors or measurement errors can introduce outliers.
Measurement Error: Outliers can be due to faulty procedures or instruments.
Natural Outlier: Sometimes, an outlier might not be due to an error. For example, in a dataset of human heights, a value like 7.5 feet would be an outlier but can be a valid measurement for an exceptionally tall person.
Sampling Error: Drawing a sample from a wrong population can introduce outliers.
Intentional Outlier: Sometimes data is manipulated to appear as outliers for fraudulent reasons, such as financial fraud.
It’s important to note that not all outliers are “bad” or erroneous. They can sometimes indicate significant findings or occurrences. In all cases, it’s crucial to understand the context of your data and the reasons for outliers before deciding how to handle them.