An outlier is a data point that differs significantly from other observations in a dataset. It can indicate variability, errors, or unusual conditions.
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Outliers can affect the mean more than the median of a dataset.
They are often identified using methods such as the IQR (Interquartile Range) or standard deviation.
In stem-and-leaf plots, outliers may appear as isolated values far from other data points.
Box plots visually represent outliers as individual points outside the whiskers.
Outliers can be caused by measurement errors, data entry errors, or genuine variation.
Review Questions
How does an outlier influence the mean and median of a dataset?
What methods can be used to identify potential outliers in a dataset?
In which graphical representation might you see an outlier as an isolated point?
Related terms
Interquartile Range (IQR): A measure of statistical dispersion, which is the difference between the first quartile (Q1) and third quartile (Q3). Used to identify outliers.
Box Plot: A graphical representation of data that shows the distribution's minimum, first quartile, median, third quartile, and maximum. Outliers are displayed as individual points.
Standard Deviation: A measure of the amount of variation or dispersion in a set of values. High standard deviation indicates more spread-out data; low indicates clustered data.