Recruiter's Glossary

Examples: Research Presentation • Method Validation • Peer Review Process

Data Validation

Term from Scientific Research industry explained for recruiters

Data Validation is the process of checking if research information is accurate, complete, and reliable before it can be used in scientific studies. Think of it like quality control for information - making sure all the numbers and facts collected are correct and trustworthy. This is crucial in research because wrong data can lead to incorrect conclusions. Scientists and researchers use various methods to check their data, from simple checks (like making sure numbers are within reasonable ranges) to complex statistical tests. This process is similar to how a restaurant inspector checks food quality, but instead of food, researchers are checking the quality of their collected information.

Examples in Resumes

Performed Data Validation on clinical trial results involving 1,000+ patients

Led Data Validation and quality assurance processes for medical research studies

Developed Data Validation protocols for environmental sampling data

Implemented Data Quality checks and Data Validation procedures for laboratory results

Typical job title: "Data Validators"

Also try searching for:

Data Quality Specialist Research Data Analyst Data Quality Analyst Clinical Data Manager Research Data Validator Data Quality Control Specialist Data Integrity Specialist

Where to Find Data Validators

Professional Organizations

Online Communities

Job Resources

Example Interview Questions

Senior Level Questions

Q: Can you describe a time when you developed a data validation strategy for a large research project?

Expected Answer: Look for answers that show experience in creating comprehensive validation plans, managing teams, and handling complex data sets. They should mention how they ensured data quality across different sources and handled any challenges that came up.

Q: How do you handle conflicting data from different sources?

Expected Answer: Strong answers should include systematic approaches to investigating discrepancies, documentation procedures, and experience coordinating with different departments or teams to resolve conflicts.

Mid Level Questions

Q: What methods do you use to ensure data consistency?

Expected Answer: Candidates should describe practical checking methods, such as comparing data across different timepoints, looking for unusual patterns, and verifying information against source documents.

Q: How do you document data validation procedures?

Expected Answer: Look for answers that show understanding of clear documentation practices, including creating standard operating procedures, maintaining validation logs, and recording any changes or corrections made.

Junior Level Questions

Q: What basic checks do you perform when validating data?

Expected Answer: Candidates should mention simple validation steps like checking for missing values, confirming data is within expected ranges, and identifying obvious errors or outliers.

Q: How do you handle finding an error in the data?

Expected Answer: Look for answers that show understanding of proper error reporting procedures, documentation of issues found, and knowing when to escalate problems to supervisors.