Any observation that lies at an abnormal distance from other observations is known as an outlier. It indicates either a variability in the measurement or an experimental error.
In the context of data analytics, an outlier refers to a data point or observation that significantly deviates from the rest of the data in a dataset. Outliers can arise due to various reasons such as measurement errors, data entry errors, natural variability in the data, or even genuine anomalies in the phenomenon being studied.
It’s important to identify outliers because they can have a disproportionate influence on statistical analyses and machine learning models, potentially leading to biased results or inaccurate predictions. Outlier detection techniques, such as statistical methods (e.g., Z-score, modified Z-score, boxplots), machine learning algorithms (e.g., isolation forest, k-nearest neighbors), or domain knowledge-based approaches, are often employed to detect and handle outliers appropriately based on the specific context and goals of the analysis.
In an interview setting, it’s essential to not only define what an outlier is but also demonstrate an understanding of the potential impacts of outliers on data analysis and the methods available to detect and handle them effectively. Additionally, providing examples or scenarios where outliers might occur and discussing strategies for dealing with them can further demonstrate proficiency in this area.