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Data analysis is an essential component of decision-making in a variety of areas which range from healthcare and finance to scientific research and marketing. However there are outliers in a dataset, or data deviations from the norm can affect the accuracy of analytical findings and reduce the accuracy of insights derived from data. This article examines the significance of managing outliers in a database and examines the effect of outliers in statistical analyses as well as the risks they could bring, as well as the different ways to identify and control these outliers. Data Science course in Pune
Understanding Outliers:
Outliers are those data instances that are significantly different from all observations within the data set. They can be caused by different reasons, including measurement errors, anomalies that are not detected in the experiment, or even genuine extreme instances. The identification and correction of outliers is crucial to ensure the validity of statistical analysis and to avoid misinterpreting the results.
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