An Average Salary of IDR 54.5 Million? Hold On: 4 Questions to Ask Before You Believe a Number

Imagine you are job hunting. A job ad says, in bold letters: “Average employee salary: IDR 54.5 million per month!” Your heart skips, your CV is sent off, and you are already picturing your mortgage getting paid off early.

Then on your first day at the office you learn the small print. The company has ten people. Nine of them earn IDR 5 million. The tenth, who happens to be the owner, pays himself IDR 500 million.

Do the math yourself: (9 × 5 million + 500 million) ÷ 10 = 54.5 million. The ad did not lie. The number is correct. But do you feel deceived? Of course you do.

This is exactly why I have stayed in statistics for so long, and why it also frustrates me. Statistics almost never lies. What lies is the way we present and read it. The keynote I once gave at a university took its title from this idea: How to Lie with Statistics. Not to teach people to lie, but so that they are not easily lied to.

After more than twenty years working with surveys, political research, academic studies, and dashboards for oil and gas and automotive companies, I have found a repeating pattern. Almost every misleading number gets through because people forget to ask four simple questions. Let us go through them one by one, slowly, without formulas that give you a headache.

1. An average of what, and who was left out?

Back to that office. Besides the average (the mean), there is another measure called the median: the middle value when all the data is lined up from smallest to largest. In that office the median salary is IDR 5 million. And IDR 5 million is what most employees actually experience.

Why is the gap so large? Because the mean is like an overly sensitive scale: a single extreme value is enough to push it. Imagine weighing rice on a gold scale. The reading may be accurate down to the milligram, but the instrument does not suit what you need. For skewed data, such as income, house prices, or savings balances, the median is almost always more honest.

A practical rule: if the mean and the median are far apart, an extreme value is “pulling” the number. Do not stop at one number. Look at the spread too.

The second question matters just as much: who is not being counted? Take a customer satisfaction survey filled in only by active customers. The result will almost certainly look good, because disappointed customers have already left and are not on the respondent list. Experts call this survivorship bias: we only see those who stayed, and assume everything is fine. If you judge a company by the employees who remain, you may miss the stories of those who resigned.

2. How many people were surveyed, and how were they chosen?

“The survey involved 10,000 respondents!” Sounds convincing. But the more important question is where those 10,000 people came from.

If a survey link is shared in one chat group full of like-minded people, ten thousand responses are really just one voice repeated ten thousand times. Conversely, 400 people chosen at random from a clearly defined population can give a far more trustworthy picture.

On margin of error, here is an easy rule of thumb for a survey with a 95% confidence level:

• A sample of 300 people: margin of error of about ±5.7%
• A sample of 400 people: about ±4.9%
• A sample of 1,000 people: about ±3.1%

So if a random survey says 52% of respondents choose A and 48% choose B with a sample of 400, that four-point gap is still swallowed by the margin of error. A headline saying “A leads” becomes too bold. This is why surveys and quick counts demand discipline: sample size, sampling method, and margin of error must be stated openly. If all three are missing, treat the number as an opinion wearing a statistics costume.

3. Compared with what?

A number without a comparison is like a 3-0 score with no idea who the opponent was: it sounds great, but nobody knows what it means. “Our sales grew 20%.” Good, until you learn that the whole market grew 35% in the same period. Compared with the market, you actually fell behind.

A fair comparison has three conditions: per capita (so large regions do not automatically win or lose), the same period (do not compare Eid season with an ordinary month), and the same definition (the one most often forgotten).

A telling example is in crime data. Big cities almost always lead in the number of crime cases, but that is because they have many residents. Once you divide by population, the ranking can flip.

Business dashboards work the same way. I once helped build a 21-page dashboard to monitor six regions at an oil and gas company. No dashboard, however beautiful, is useful if the numbers on each page have no clear comparison: against target, against last month, or against another region. A director does not need many charts. A director needs charts that answer the question “are we doing well or badly?”.

4. Who made this chart, and what do they want to show?

The fourth question is about staying alert. Charts are a very powerful tool of persuasion, which also makes them the easiest to abuse. Some tricks that appear often:

• A truncated axis. The vertical axis does not start at zero, so a small difference looks like a big jump.
• Mismatched scales. Two charts side by side with different scales give an impression of similarity that is not real.
• A hand-picked period. A trend looks like a sharp rise only because the chart starts exactly at the lowest point.
• Percentages without the base number. “Up 300%” can mean going from 1 case to 4 cases.

That does not mean every chart maker intends to deceive. Often it is simple carelessness. But as readers, we do not need to know the intent. We only need to check the axis, the scale, the period, and the base figure before we believe.

One small habit that changes how you read numbers

The four questions boil down to one habit: do not accept a number as fact; treat it as a claim to be tested. A good claim has nothing to fear from testing. A shaky claim looks shaky the moment you ask, “of what, from whom, compared with what, and who made it?”

This habit helps everywhere: reading the news, reading an office report, judging an investment offer, even writing a thesis. Students who get used to asking this usually level up quickly when writing their results and discussion chapters, because they stop reporting numbers and start interpreting them.

And if one day the data in front of you feels too complicated, or its conclusion feels off, that is not a sign you are not smart enough. It is a healthy signal that something needs to be checked again.

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