You will be able to choose between the mean and the median and say what each hides.
A recruitment post shows up in Priya's LinkedIn feed: "Join our sales team. Our agents earn an average of S$12,000 a month." Her cousin joined a team like that last year and earns nowhere near it. Both things can be true at the same time, and the word that makes it possible is "average".
The numbers in this lesson are made up to show how averages work. They are not real figures for any company or job.
When people say "average" they usually mean the mean: add up all the values and divide by how many there are. The median is the middle value when you line them all up from smallest to largest. With an even number of values, it is halfway between the two in the middle.
Most of the time you do not need to think about the difference. If ten colleagues have similar commutes, the mean and median commute will be close. The difference matters when a few values are far from the rest.
Here is a sales team of ten that could produce that recruitment post. Nine agents earn S$2,000, S$2,500, S$3,000, S$3,000, S$3,500, S$4,000, S$4,500, S$5,000 and S$6,000 a month. The top agent earns S$86,500. The total is S$120,000, so the mean is S$12,000, exactly as the post says.
Now line them up and find the middle. The fifth and sixth agents earn S$3,500 and S$4,000, so the median is S$3,750. Nine of the ten agents earn less than the "average". The post is accurate and tells a new joiner almost nothing about what they are likely to earn.
The mean uses every value, so one extreme value moves it a lot. Take the top agent out of that team and the mean of the other nine drops from S$12,000 to about S$3,722. The median of the nine is S$3,500. One person moved the mean by more than S$8,000, while the median barely moved.
That is the whole difference in one line: the mean is pulled by extreme values, and the median is not. Neither is wrong. They answer different questions. The mean tells you the total shared out equally, which is useful if you are a company planning a payroll budget. The median tells you what a typical person sees, which is usually what you want to know if you are one of those people.
Some kinds of data are naturally lopsided, with a long tail on one side. Statisticians call this skewed data. Income is the classic case. Most people earn within a fairly narrow band, and a small number earn many times more. Those few high earners pull the mean up, so the mean income sits above what most people actually take home.
Property prices behave the same way. Picture a made-up condo where seven units sell in a year: six between S$1.15 million and S$1.38 million, and one penthouse at S$4.8 million. The mean price is about S$1.77 million. The median is S$1.3 million. A headline saying "average price at the development hits S$1.77 million" would be technically correct and would describe none of the seven sales.
This is why official income statistics are usually reported as medians. Singapore's Ministry of Manpower, for instance, leads with the median when it reports monthly income from work. If you want to know what a job pays, look at MOM's Occupational Wage Tables, MyCareersFuture or a recruiter's salary guide, and check which measure each one uses.
So when you see "average" attached to income, prices, commissions, wealth or anything else where a few cases can be very large, ask which average it is. If the source does not say, assume it could be the mean and treat it with care.
Even the median only tells you about the middle. Two teams can have the same median salary and look nothing alike. In one team, everyone earns within a few hundred dollars of the median. In another, half earn far less and half earn far more. A single number cannot show that.
The spread tells you how far values sit from the middle. You do not need technical measures to ask about it. Plain questions work: what is the lowest and highest? What do the bottom quarter and top quarter look like? Some salary sources show a range around the median, such as the 25th and 75th percentiles, which tells you where the middle half of people fall.
For the recruitment post, the useful question is something like: "What does an agent in their first year usually earn, and what's the range?" That gets you closer to your own likely outcome than any single average.
You will meet this in reports all the time. Average deal size can be pulled up by one huge client. Average response time can be dragged out by a handful of tickets that sat for weeks. Average time to hire can hide that most roles fill quickly and two senior roles took a year. In each case, ask for the median alongside the mean, and look at the extremes separately. They are often the most interesting part of the data.
The quickest way to feel this is to build the example yourself. In the activity below you will make up a team of ten with one very high earner and see how far apart the mean and median land.
Take the salaries of ten people in a made-up team including one very high earner, and compare the mean and median.
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