What are outliers?
Outliers are data points that are significantly different from other data points in a dataset. They are observations that lie an abnormal distance away from other observations. In other words, outliers are values that fall outside the typical range of values in a dataset. Outliers can occur due to various reasons such as measurement errors, natural variations, or they may actually represent important and meaningful observations. Detecting and handling outliers is important in data analysis and statistical modeling as they can have a significant impact on statistical measures and can affect the accuracy of models and predictions.
Outliers in statistics are data points that significantly deviate from the overall pattern of the dataset. They are observations that lie an abnormal distance away from other values in a random sample from a population. Outliers can be either very high values (known as upper outliers) or very low values (known as lower outliers).
Outliers can occur due to various reasons such as measurement errors, experimental errors, data entry errors, or they could represent true but rare occurrences in the data. Outliers can affect the analysis of data and can have a substantial impact on statistical measures such as the mean and standard deviation.
Detecting outliers involves examining the data and determining if any values appear to be unusually distant from the bulk of the data. There are several methods for identifying outliers, including visual inspection of a scatterplot or boxplot, calculating the z-scores of the data points, or using statistical techniques such as the Interquartile Range (IQR) or the Modified Z-score method.
Once outliers are identified, it is important to decide how to handle them. Depending on the nature of the data and the analysis being conducted, outliers can be managed by either removing them from the dataset, transforming them to reduce their influence, or treating them as a separate group for further analysis. The appropriate approach for handling outliers depends on the specific context and objectives of the analysis.