Outlier Detection with IQR

Medium

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Using the IQR method, a data point is typically classified as an outlier if it falls below Q1 - 1.5IQR or above Q3 + 1.5IQR. What is a key limitation of this approach?

Multiple Choice

It can only detect outliers in normally distributed data and fails for all other distributions It may flag legitimate data points as outliers in skewed distributions since it assumes rough symmetry It requires knowing the population mean and standard deviation It cannot detect outliers when the sample size exceeds 1000

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Outlier Detection with IQR

Multiple Choice

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