**Child:** Imagine you and your friends are lining up according to your heights, from shortest to tallest. The person in the exact middle of that line has the ‘median’ height. If there’s no one exactly in the middle because there’s an even number of you, then we take the average (or middle) of the two people who are in the center. This ‘median’ height gives us a good idea of how tall a typical person in your group is.

**Teenager:** Say you’re looking at the ages of all the players on a soccer team, and you want to find the ‘middle’ age. You line up all the ages from youngest to oldest and find the one in the middle. That’s the median. It can be really useful because it’s not affected by very high or very low values. So, even if you have one player who’s much older than everyone else, it won’t throw off your median.

**Undergrad majoring in the same subject:** In statistics, the median is a measure of central tendency that indicates the middle value in a data set when the values are arranged in order of magnitude. If the data set has an odd number of observations, the median is the middle number. If it has an even number of observations, the median is calculated as the average of the two middle numbers. The median provides a robust measure of central location that is not influenced by outliers or extreme values, unlike the mean.

**Grad student:** The median serves as a position-based measure of central tendency, dividing a data distribution into two equal halves. Unlike the mean, it is not susceptible to the influence of outliers and therefore provides a more resistant measure of a ’typical’ value in skewed distributions. The calculation of the median involves ordering the data and selecting the middle value, or the average of the two middle values in the case of an even-sized data set.

**Colleague (Fellow Researcher/Engineer):** The median is a crucial statistical measure of central tendency, particularly valuable when dealing with skewed distributions or when it’s necessary to minimize the impact of outliers. Unlike the mean, which factors in all values and can be skewed by exceptionally large or small values, the median relies solely on positional information. It is the value separating the higher half from the lower half of a data sample, providing a robust and resistant measure of the data’s central location, which is vital in fields such as robust statistics and non-parametric statistics.