When you have a collection of numbers — say, the time it takes to render a web page — it's instinctive to reach for the average as a way of summarizing the data. This single number then serves to represent the whole: "the site renders in an average of 50ms".
Here's an obvious observation: any summary of numbers like this is exactly the problem of statistics and statistics is filled with more principled ways of looking at data. But statistics is also intimidating and opaque so instead let's talk about it informally.
If I told you that the average was 50ms, that statement conveys a mental image of the data: you'll intuit that it's usually around 50ms, sometimes faster, sometimes slower. You'd feel tricked (or just annoyed) if I then said that the underlying data was nine page loads at 10ms and one at 410ms. It's a trick because while the statement is true by the mathematical meaning of average, when we talk about averages we're actually conveying both the mathematical meaning and the shape of the data.
Let's make that more formal. The mental image that "average" implies is a bell curve; that is to say, a normal distribution. A normal distribution is defined by two parameters, the mean and the deviation. Rather than overwhelming you with a list of sample values, I instead told you a picture of a normal distribution by telling you the parameter(s) of it.
This is why the average is frequently useful and sometimes totally misleading. Many data sets are normally distributed, or at least symmetrically distributed around the average, and for those the picture implied by the average works. But plenty of data instead matches other distributions (for example, anything "long-tailed") and for those the average is unhelpful.
Some, when presented with this point, would cry for better statistical literacy. They'll plug data into formulas and brandish p-values and confidence intervals. That approach may be useful when everyone knows how to read those numbers but in my experience that usually isn't the case. (Frequently the formulas are applied wrong, or in circumstances where they're the wrong formula anyway.)
Instead the point I'd like to make is that there is a useful middle ground between "go read a stats textbook" and "math is hard, let's use the average". It's useful to be able to talk about data without needing to produce every last data point; if you can swoop your hands in the air and say "it looks like this" then that is still communication and it's still better than a misapplied average.
Here's an example of that distinction. Life expectancy is a statistic on the average lifetime of a human, which is currently around 80 for developed countries. But lifetimes are not normally distributed: Wikipedia says in 1600s England life expectancy was around 40 years, but that's primarily due to infant mortality; aristocrats who made it to age 21 could expect to live into their 70s, which is pretty comparable to today's numbers.
As with any data visualization — even data visualization comprised of a single number — the first step before diving into numbers and pictures is to decide which question you're asking. If you're asking "where do people live the longest?" you will pick a different metric than one attempting to measure cultural progress in health care. And going back to the website example, if the question is "Is my site slow?" then maybe quantiles are more interesting, because they lead to statements like "95% of users load it in under X milliseconds".