That really depends on the data. I'm expecting you know the formal definition. What it effectively does is to ignore large outliers / weigh large groups of outliers less heavily, that could otherwise dominate your data. For example: If you are in a room with 10 minimum wage employees, Bill Gates, and Elon Musk, the mean networth of the group would be something like 20 billion, but the median would be more like 1k. So for a lot of analyses about "human" groups I'd use the median to see what a "typical" person looks like. (In either case you should look at the actual distribution, if you want a good analysis) More on reddit.com

Let's say you have 99 randomly selected people in a room with Jeff Bezos. The average net worth of everybody in the room will be approximately $2 billion, because Jeff Bezos is worth $200 billion and there are 100 people in there, and the other people have effectively zero money (compared to $200 billion). But saying "the average net worth in the room is $2 billion" is utterly meaningless because it's really just one person bringing up the average for everybody else. So a more meaningful picture might be the median or the mode... the median is the middle person's net worth. The mode is the most common net worth. These give you a better picture of what the "average" person looks like in the room, than what the actual average tells you. More on reddit.com

The mean (or average) tends to be more useful when you are dealing with data that has a normal distribution, meaning there's like an even spread of data points. Imagine you had 100 students and 20 got A's, 20 got B's, 20 got C's, 20 got D's and 20 got F's. The average will be a pretty good indicator of what the average grade score was. But if you have a much more skewed data set, like say we're talking about real estate costs of houses in a city, then the average becomes less important. For instance, say 10 houses out of 100 are worth $100 million but the other 90 houses are worth between $200,000 to $400,000. Well, those $100 million houses are gonna screw up the average cost a lot, making you think all the houses are super expensive in that city. But if you use the median you get a better idea of how the costs are actually distributed. More on reddit.com

From a data engineer's perspective, average is much easier to compute than median for large datasets. To calculate an average for a long list of values, all you need is a sum and a total. The database can read each value once, add it to the sum, then release it from memory and move on. For a median however, you need to sort all the values and find the value in the middle, which requires more compute time and memory. There are a few algorithms that improve this but none of them scale very well. None of this matters if you only have 100 or 1,000 values, or if you're doing one-time calculations. But for us, our data analysts ask us to produce reports from tables with millions of records, with live dashboards that constantly get updated. So unless there's a really good reason to use a median instead of a mean, we'll go with the mean. More on reddit.com