In sampling, an outlier refers to a data point that is significantly different from the other data points in the sample. Outliers can occur due to a variety of reasons, such as measurement errors, data entry errors, or unusual observations.
Outliers can have a significant impact on the results of a statistical analysis or survey, as they can distort the mean, median, and other measures of central tendency. Therefore, it's important to identify and handle outliers appropriately in a sample.
There are several techniques for detecting and handling outliers, including visual inspection of data plots, statistical tests, and data cleaning methods. For example, one approach to handling outliers is to remove them from the sample or adjust them to a more appropriate value. However, it's important to use appropriate statistical methods and consider the potential impact of outlier removal on the overall results of the analysis.
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