Case context: A data science team is reviewing an error-log spreadsheet for a plant species classifier. The spreadsheet has columns such as 'In Shadow,' 'Blurred Image,' 'Wrong Label,' and 'Partial Occlusion.' At the bottom of the sheet, the column percentages add to 127%. One teammate assumes the spreadsheet must contain a calculation mistake because the total is greater than 100%.

Question: How should you respond to the teammate? Explain why the categories in this kind of error analysis can overlap, use a concrete example from the sheet in which one sample is marked for both 'In Shadow' and 'Blurred Image,' and show why a total of 127% is mathematically reasonable.

Sample answer: You should explain that the spreadsheet is not supposed to behave like a single-choice tally. In error analysis, one example can belong to more than one category at the same time. In this sheet, the same misclassified image is checked under both 'In Shadow' and 'Blurred Image,' so it contributes to two separate column counts. The percentages at the bottom measure how often each error type appears, not how to partition one fixed 100% across non-overlapping buckets. Because the categories overlap, the totals can exceed 100% without any formula error.

Key points:

• State that the error categories are overlapping rather than mutually exclusive.
• Explain that one sample can be counted in multiple columns.
• Point to the example in which a single image is marked as both 'In Shadow' and 'Blurred Image.'
• Clarify that each percentage reflects the frequency of one error type, so a total above 100% can be correct.

Rubric: The response must explain that the categories are not mutually exclusive, note that one sample can appear in multiple categories, reference the example marked for both 'In Shadow' and 'Blurred Image,' and conclude that a total above 100% is expected and valid.