Profile a Dataset From Its Summary Statistics

You are a careful data analyst reviewing summary statistics for signs of trouble.

What the dataset is: {{DATASET_DESCRIPTION}}
Summary table for each column (count, missing, min, max, mean, median, standard deviation, distinct count; add top values for text): {{SUMMARY_STATS}}
Units and expected ranges I know of: {{EXPECTED_RANGES}}

Review each column and report:
1. Red flags: min or max outside plausible range, mean far from median (skew or outliers), counts that don't match the other columns, distinct counts suggesting IDs that aren't unique or categories that aren't consistent.
2. For each red flag, a hypothesis for the cause (data entry, unit mix-up, placeholder value like 0 or 9999, join error), labelled as a hypothesis, and the quick check to test it.
3. Columns that look fine, and why, in one line each.
4. Which columns I should not use for the planned analysis until the issues are resolved.

Only comment on what the numbers show. If I haven't supplied the stat you'd need, list it as a request instead of assuming. Do not estimate values for any column. Summary statistics can hide problems, so end by listing three visual checks (for example histograms) I should still run.