Summary  
This chapter covers how to compute mean and median in code to summarize a dataset and highlights that the mean is more affected by outliers while the median provides a more robust central value.

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.

Learn core statistical concepts used in data analysis with Python. The course covers descriptive statistics, including mean, median, mode, variance, and standard deviation, as well as sampling, probability distributions, the Central Limit Theorem, and outlier detection.

Mean or Median

Are there differences between these two measures?

Conclusion