The Iris data is very clean. The only real issues are one duplicated row and a few mild outliers within species, none of which look like errors. **Nothing wrong** - **Missing values:** None. All 150 rows have values in all four measurements and in `species` (step-0). - **Inconsistent formats:** None. `species` has exactly 3 distinct values and the format check found 0 issues (step-0). - **Impossible values:** None. The ranges are plausible for iris flowers measured in centimetres (step-0): - sepal length 4.3–7.9 - sepal width 2.0–4.4 - petal length 1.0–6.9 - petal width 0.1–2.5 - There are no zeros, negatives or sentinel values such as -1 or 999. - **Mixed units or sources:** No evidence of either. All rows come from one batch, one file and one source system (steps 0, 3 and 4). There is no unit column, so cm is an assumption from the value ranges rather than something the data states. **Issues found** 1. **One duplicated row:** One group of identical rows exists, meaning 1 surplus copy (steps 1 and 3). This is minor, but it slightly inflates the counts. The classic Iris data is known to contain a few duplicate measurements, so it may be genuine. I did not pull up which row it is. 2. **Mild outliers within species:** I counted outliers against each species' own spread. There are 13 flagged rows, made up of 1 in sepal length, 5 in sepal width, 5 in petal length and 2 in petal width (step-1). The flagged rows are listed in step-5. - Most are in setosa. Examples are a sepal width of 4.4, a sepal width of 2.3, and petal lengths of 1.0, 1.1 and 1.9, all at the edges of setosa's range. - The others are a versicolor with a small petal length of 3.0 and a few virginica with extreme sizes (sepal length 7.9, petal length 6.7, sepal width 2.2). These outliers are natural variation among real specimens, not data-entry errors. Keep them unless a specific analysis is sensitive to extremes. Deduplicating the one repeated row is optional.