How many potential outliers are in the data?

Question

BlackTom AI · Accepted Answer

Question restatement: How many potential outliers are in the data?
Options presented: 3, 2, 1, 4, 0

Option 3: '1' — This would mean there is a single data point that lies beyond the threshold used for outlier detection. Depending on the rule you apply (IQR method, Z-score, or other criteria), it is entirely plausible for only one point to be flagged as an outlier, but it may be unlikely in larger datasets or under stricter thresholds.

Option 2: '2' — This option asserts that there are two potential outliers. Under many common rules (for example, the 1.5 IQR rule or a commonly used Z-score cutoff), two data points routinely fall outside the typical bounds in moderately sized datasets. If the given data indeed produces two observations beyond the cutoff, this option would be consistent with a moderate number of extreme values.

Option 1: '3' — Claiming three outliers suggests a somewhat heavier tail or more extreme deviations. Depending on the distribution and the chosen threshold, reaching three outliers is possible, but it would indicate a more pronounced departure from central tendency and spread than a dataset with only two flagged points.

Option 4: '4' — Four outliers would indicate a substantial number of extreme observations. This is less common in many real-world datasets unless the data are highly skewed, contain multiple anomalous clusters, or the threshold for outliers is relatively lenient.

Option 0: '0' — Saying there are no outliers implies all observations lie within the defined bounds. If the data show some points beyond typical cutoffs, this option would be inconsistent with those observations; however, it is possible in perfectly normal or tightly distributed data with conservative thresholds.

Note on the implied answer: The provided answer list indicates '2' as the correct choice. In many standard outlier-detection schemes (such as using the 1.5 times IQR rule or a standard Z-score cutoff), it is common to encounter two observations flagged as outliers in moderately sized datasets, aligning with the second option. Without the raw data or the exact threshold, other options reflect alternate scenarios that could occur under different distributions or rules, but two outliers is a plausible and frequently observed outcome.

How many potential outliers are in the data?

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