Summary  
This chapter demonstrates how to calculate the mean and median of a dataset, comparing their susceptibility to outliers and showing when each measure better represents central tendency.

General domain of usage  
Data analysis

**Mean** and **median** are similar concepts that help identify the average value of a sample and provide insight into the overall data.

## Are there differences between these two measures?

Consider an example that compares the **mean** and **median** salaries of IT specialists, with a focus on the last individual who has the highest salary.

## Conclusion

The **mean** value is more susceptible to extremely small or high values than the **median**. Therefore, when dealing with the values in a sequence that aren't similar, it is better to use the **median**. In our example, the **mean** value is twice as high as the **median**, making the **median** a more accurate representation.

Select lists where there is a significant difference between the mean and median values.

Build a strong foundation in statistics using Python. Learn essential statistical concepts and apply them through NumPy and pandas. Move from basic measures like mean and variance to hypothesis testing, confidence intervals, and data-driven insights with hands-on practice.

Discover core statistical principles, including data types, measures of central tendency, and key differences between samples and populations.

Learn to calculate and interpret mean, median, and mode using Python. Practice these operations with pandas to analyze real datasets.

Understand how variance and standard deviation measure data spread. Learn to compute both manually and using Python tools.

Explore how covariance and correlation describe relationships between variables. Practice calculating and comparing both metrics in Python.

Master confidence intervals to estimate population parameters. Use NumPy, pandas, and visualization libraries to calculate and interpret intervals with real data.

Learn the fundamentals of hypothesis testing and the t-test. Understand how to design, perform, and interpret tests using Python to support data-driven decisions.

Mean or Median

Are there differences between these two measures?

Conclusion