Outlier detection is crucial for data quality from "summary" of Statistics for Censored Environmental Data Using Minitab and R by Dennis R. Helsel
The identification of outliers in a dataset is a critical step in ensuring the quality and reliability of the data. Outliers are data points that deviate significantly from the rest of the data and can have a disproportionate impact on statistical analyses. These data points can arise due to errors in data collection, measurement variability, or true differences in the underlying processes being studied. Failure to detect and address outliers can lead to biased estimates, incorrect conclusions, and reduced statistical power. Outliers can skew the distribution of the data, affect the calculation of summary statistics, and influence the results of hypothesis tests. By identifying and removing outliers, researchers can improve the accuracy and precision of their analyses, leading to more robust and trustworthy results....
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