Practice Outlier Detection in Statistics
Use these practice problems to test your method after reviewing the concept explanation and worked examples.
Quick Recap
Methods for identifying data points that are unusually far from the rest, using techniques like IQR rule, z-scores, or visual inspection.
Outliers are data points that don't fit the pattern. A 7-foot student in a class of average heights, or a \10 million house in a neighborhood of \300k homes. They may be errors or genuinely unusual.
Example 1
easyThe data set is: 10, 12, 11, 13, 12, 14, 11, 50. Identify the outlier and explain how you know.
Example 2
mediumTest scores: 72, 75, 78, 80, 82, 85, 88, 90, 92, 95. A new student's score of 25 is added. How does this outlier affect the mean and median?
Example 3
mediumA scientist records reaction times (ms): 245, 260, 255, 270, 250, 980, 265, 258. Use the 1.5 \times IQR rule to determine if 980 is an outlier. Should it be removed from the analysis?
Example 4
hardA data set has mean \bar{x} = 100 and standard deviation s = 15. Using the z-score method, determine whether the values 60, 145, and 155 are outliers (using the threshold |z| > 2).