What are some common issues with mock data?
An example of an AI chatbot conversation about common mock data issues.
What are some common issues with mock data?
Common issues with mock data include lack of realism, data inconsistency, and insufficient coverage of edge cases.
How can I ensure my mock data is realistic?
To ensure realism, use real-world data patterns, incorporate variability, and consider user behavior in your mock data.
What tools can help generate mock data?
Tools like Faker, Mockaroo, and JSON Generator can help you create realistic mock data easily.
Can mock data lead to misleading results in testing?
Yes, if the mock data doesn't accurately represent real data, it can lead to misleading results and poor decision-making.
What are edge cases, and why are they important?
Edge cases are unusual or extreme scenarios that can occur in data. They are important to test to ensure robustness.
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