Recruiter's Glossary

Examples: Box Plot • Data Mining • Time Series Analysis

Variance

Term from Analysis industry explained for recruiters

Variance is a key concept in data analysis that helps measure how spread out numbers are in a dataset. Think of it like measuring how consistent or variable data points are from their average. For example, in sales data, variance would show if sales numbers are stable or fluctuate widely month to month. Analysts use variance to understand risk, make predictions, and spot unusual patterns. Related terms include 'standard deviation' and 'statistical dispersion'. Understanding variance is important for roles that involve analyzing trends, making forecasts, or managing risk.

Examples in Resumes

Conducted Variance analysis on quarterly sales data to identify seasonal patterns

Reduced cost Variance by 15% through improved forecasting methods

Applied Variance calculations to optimize inventory management

Typical job title: "Data Analysts"

Also try searching for:

Financial Analyst Business Analyst Statistical Analyst Quantitative Analyst Risk Analyst Research Analyst

Where to Find Data Analysts

Professional Networks

Online Communities

• Reddit r/statistics
• Cross Validated (Statistics Stack Exchange)
• Data Science Stack Exchange

Job Boards

Example Interview Questions

Senior Level Questions

Q: How would you explain variance analysis to a non-technical stakeholder?

Expected Answer: A senior analyst should be able to explain variance in simple terms using real-world examples, such as comparing planned versus actual budgets, and demonstrate how this analysis leads to actionable business decisions.

Q: How do you handle situations where variance analysis reveals unexpected patterns?

Expected Answer: Should discuss their approach to investigating anomalies, validating data, considering external factors, and communicating findings to stakeholders in a clear, actionable way.

Mid Level Questions

Q: What methods do you use to identify significant variances in data?

Expected Answer: Should explain practical approaches to setting thresholds for significant variations, using tools like percentage changes, historical comparisons, and industry benchmarks.

Q: How do you present variance analysis results to management?

Expected Answer: Should discuss creating clear visualizations, summarizing key findings, and providing context for why variations matter to the business.

Junior Level Questions

Q: What is variance and why is it important in data analysis?

Expected Answer: Should be able to explain that variance measures how spread out numbers are from their average and why this is useful for understanding data patterns and making decisions.

Q: How do you calculate a basic variance?

Expected Answer: Should demonstrate understanding of the basic concept of finding differences from the average and explain when you might need to calculate variance in a business context.