Few situations where the calculation of the median is particularly relevant:

1. **Skewed Data**: When dealing with skewed data distributions, the median can be a more robust measure of central tendency than the mean. Skewed distributions have a long tail on one side, and in such cases, the mean can be influenced by extreme values. The median, on the other hand, represents the middle value, making it a suitable choice for summarizing skewed data.

2. **Ordinal or Ranked Data**: If your data consists of ordinal or ranked values, where the numerical differences between values are not well-defined, the median is a preferred measure. For example, in a survey where respondents rank their satisfaction on a scale from 1 to 5, the median provides a meaningful representation of the central ranking.

3. **Outlier Detection**: The median is commonly used in outlier detection methods. Outliers are extreme values that significantly differ from the majority of the data. By comparing individual data points to the median, it becomes easier to identify potential outliers that deviate from the overall pattern.

4. **Non-Normal Data**: In cases where the data does not follow a normal distribution, the median can provide a better understanding of the central tendency. While the mean is affected by extreme values, the median remains relatively stable, making it useful for summarizing non-normal data.

5. **Data with Missing Values**: The median can be useful when dealing with datasets that contain missing values. Unlike the mean, which requires complete data, the median can still be calculated even if some values are missing. It helps to provide an estimate of the central value in the presence of missing data.

6. **Data Analysis with Skewed Distributions**: The median is often used in fields such as economics, finance, and income analysis, where data distributions tend to be skewed. By calculating the median, you can gain insights into the central value that is representative of the majority of the data.

Hope it helps:credit AI

Two simple examples:

1 - household prices

2 - personal income

where median provides better information than the average (mean)