Domain knowledge is subject-matter understanding that gives data its meaning. It tells you what values, patterns, relationships, and results make sense for the business, so you can separate useful findings from misleading ones. section 3.2 section 3.1
Use domain knowledge throughout an analytical workflow:
- Frame research questions around the business issue, stakeholders, data context, goals, and constraints rather than asking for generic analysis. section 3.1
- Check whether charts, values, and relationships have plausible shapes or meanings before accepting an analytical result. section 3.2
- Judge whether a correlation or regression result matters to the business; statistical significance alone does not establish usefulness. section 3.2
- Combine automated checks with manual inspection when correcting data-entry errors or deciding how to treat outliers. section 2.5.3
- Treat AI-generated methods, recommendations, and code as proposals: give general prompts, refine them with specialist knowledge, and test the results. section 1.2.3
Recognizing a suspicious value
Suppose nearly every transaction is from New York and one is from Los Angeles. A frequency check such as df['column_name'].value_counts() can flag the unusual value, but domain knowledge determines whether it is a data-entry mistake or an important exceptional transaction. Validate the record before removing, capping, transforming, or accepting it. section 2.5.3
