Based on our in-class discussion, which of the following is not a potential cause of outliers?

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BlackTom AI · Accepted Answer

Question restatement: Based on our in-class discussion, which of the following is not a potential cause of outliers?

Answer options to analyze:
- Natural variation
- Data entry and measurement errors
- Sampling problems
- Visualization errors

Option 1: Natural variation
- Explanation: Outliers can arise from natural variation in the data-generating process. If a population includes rare or extreme values due to inherent diversity, those values can legitimately be outliers. This option correctly identifies a plausible source of outliers in many datasets.

Option 2: Data entry and measurement errors
- Explanation: Mistakes during data entry (e.g., transposing digits) or measurement errors (instrument calibration issues) can produce values that are far from the true measurement, creating outliers. This is a well-known and common source of spurious outliers, so this option is a valid potential cause.

Option 3: Sampling problems
- Explanation: Sampling issues, such as selecting a non-representative sample, biased sampling frames, or missing subpopulations, can yield outliers that reflect sample-specific peculiarities rather than the population as a whole. This is also a recognized cause of outliers when the sample does not capture the underlying population distribution properly.

Option 4: Visualization errors
- Explanation: Visualization errors refer to mistakes or limitations in how data are plotted or displayed (e.g., mis-scaled axes, truncated ranges, or inappropriate chart types) that can mislead the viewer into perceiving extreme values as outliers or masking true outliers. While such errors can affect interpretation, they do not create genuine statistical outliers in the data—they are presentation artifacts. In that sense, visualization errors are not a genuine cause of outliers in the data-generating process, though they can influence how outliers are perceived.

Summary of incorrectness and correctness: The first three options describe real, data-generating or data-handling issues that can produce true outliers in the dataset. Visualization errors describe how the data are displayed rather than a true anomaly in the data itself, so they are not a potential cause of outliers in the data-generating sense.",

SU25-BL-BUS-A314-12480 Checkpoint 4

Based on our in-class discussion, which of the following is not a potential cause of outliers?

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