Recruiter's Glossary

Examples: T-Test • Power Analysis • Skewness

Mean

Term from Analysis industry explained for recruiters

Mean is a basic but important mathematical concept used in data analysis. It's what most people call an "average" - adding up all numbers and dividing by how many there are. When you see this term in resumes, it often indicates experience with basic statistical analysis. Data analysts and researchers use means to understand typical values in their data, like average customer spending, typical response times, or normal production rates. While it's a simple concept, understanding how and when to use means (versus other statistical measures like median) is a key skill in data analysis roles.

Examples in Resumes

Calculated Mean values to analyze customer satisfaction trends

Used Mean and standard deviation to identify outliers in production data

Applied Mean analysis techniques to evaluate employee performance metrics

Typical job title: "Data Analysts"

Also try searching for:

Statistical Analyst Research Analyst Data Scientist Business Analyst Quantitative Analyst Market Research Analyst

Where to Find Data Analysts

Online Communities

Professional Networks

Job Boards

Example Interview Questions

Senior Level Questions

Q: When would you choose to use mean versus median in your analysis?

Expected Answer: A senior analyst should explain that means are best for normally distributed data, while medians are better when there are extreme values that could skew the average. They should provide real-world examples, like using median for house prices and mean for temperature readings.

Q: How would you explain the limitations of using means in data analysis to non-technical stakeholders?

Expected Answer: Should demonstrate ability to communicate technical concepts simply, explaining how averages can be misleading and when other measures might be more appropriate, using practical business examples.

Mid Level Questions

Q: How do you handle outliers when calculating means?

Expected Answer: Should explain methods for identifying outliers and different approaches to handling them, such as removal, trimmed means, or using alternate measures, with focus on maintaining data integrity.

Q: What's the difference between mean, weighted mean, and moving average?

Expected Answer: Should be able to explain these concepts in simple terms, using practical examples like customer ratings (weighted mean) or stock prices (moving average).

Junior Level Questions

Q: How do you calculate a basic mean?

Expected Answer: Should be able to explain the simple process of adding all values and dividing by the count, and when this calculation would be useful in business contexts.

Q: What can cause a mean to be misleading?

Expected Answer: Should understand basic concepts like how extreme values can affect the mean, and why this might not represent the typical case in certain situations.