Part I · Chapter 4

The anatomy of a bad statistic

Most articles about computing careers are either too negative or too positive. This chapter shows you how to tell the difference yourself.

This chapter does it with four real cases. You have probably seen at least two of them.

Case one: the unemployment chart everyone shared

In 2025 a chart spread across the internet. It showed that computer science graduates had 6.1% unemployment, and computer engineering graduates had 7.5%. People used it to argue that studying computing was now a mistake.

The chart was real. The source was real. It came from a Federal Reserve Bank of New York dashboard, which is a serious institution. Nothing was invented. It was still close to useless, and here is why.

Problem one: no interval

The numbers came from survey data. Every survey has uncertainty, because it asks some people rather than all people.

Good statistics report that uncertainty as a confidence interval, which is a range showing how sure you can be. The Economic Innovation Group checked this one. For computer engineering, the real range ran from roughly 4% to 11%.

Think about what that means. The true figure might be 4%, which is excellent. It might be 11%, which is bad. The chart showed 7.5% as if it were a fact.

A single number without a range is a rumour. Learn this sentence. It will protect you more than anything else in this book.

Problem two: the same data said something else

The same dataset contained another number. About 90% of computer science graduates were employed.

Both numbers are true at once. Unemployment counts only people actively looking for work. The employment rate counts everyone. They answer different questions, and they leave very different impressions. Nobody shared the second one, because it was not alarming.

Problem three: the date

The chart used data collected in 2023, published later. People shared it in 2025 as a description of the market that year.

Two years is a long time in this field. Chapter 9 explains what changed in between.

What to take from case one

The source was excellent. The number was real. The conclusion was still wrong, because the number arrived with no range, no context, and no date attached.

Case two: the COBOL numbers that everyone repeats

You will meet these figures if you read anything about mainframes.

COBOL, they say, runs 70% of the world’s business transactions. It handles 95% of cash machine withdrawals. There are 220 billion lines of it still running.

These numbers appear everywhere. Follow them backwards and something strange happens.

Most articles cite a Reuters piece from 2017. The Reuters piece took the figures from Rocket Software, a company that sells mainframe products. Rocket Software’s figures trace back to a survey by a firm called DataPro. That survey ran in 1997, and it asked 421 companies.

So a claim about the whole world’s banking system in 2026 rests on 421 companies answering a question three decades ago. Nobody has measured it properly since.

The lesson: follow the chain to its root

This is a citation chain. Article cites article cites report cites survey. Each step looks respectable. The number gets stronger as it travels, because each new article states it with more confidence than the last.

The number is not stronger. It is just older.

When a figure matters to your decision, follow it back until you find the original measurement. Then look at that measurement. How many people? What year? Who paid?

Often the chain simply stops, and there is nothing at the end of it.

Does this mean COBOL is unimportant?

No, and this matters. Chapter 25 recommends mainframe work.

The point is narrower. COBOL genuinely runs critical banking systems. But the specific percentages everyone quotes are not evidence. The career is real. The statistic is decoration.

You can believe a claim and still reject the bad number used to support it.

Case three: the number that disappeared

For years, one figure dominated every article about cybersecurity careers. The world needed 4.8 million more security professionals. There was a huge gap.

ISC2, a large security membership body, published it in 2024. It was a 19% rise on the year before. Journalists repeated it constantly.

In 2025, ISC2 published its workforce study again. This time it had a record 16,029 respondents, so the study was bigger and better than before.

The 4.8 million figure was not in it.

ISC2 did explain it. Respondents in 2024 and 2025 had put critical skills ahead of headcount, and the study says that is why no gap estimate was included that year. The new framing was about skills gaps rather than headcount. It reported that 95% of teams lacked at least one specific skill, and that 59% called the gap critical or significant.

Note what the organisation did here, because it is the honest move and it is rarer than the alternative. It did not quietly drop the number and hope nobody noticed. It said why it had stopped publishing it, and its stated reason is the same reason this chapter is about to give you.

The lesson: absence is information

When an organisation stops publishing its own headline number, pay attention. That silence tells you something.

Here it tells you something useful for your career. “Millions of open jobs” was never the right description. Employers do not want any 4.8 million people. They want specific skills they cannot find. Those are different problems, and they lead to completely different advice for someone entering the field.

Chapter 25 builds on this. The security shortage is real, and it is not a shortage of bodies.

Case four: shortages announced by the people selling the cure

Now the most useful question in this chapter. Who profits if you believe this?

Two examples.

The COBOL shortage. Article after article warns of a coming crisis as mainframe programmers retire. Look at who publishes these warnings. Training companies that sell COBOL courses. Staffing firms that place mainframe contractors. Consultancies that sell modernisation projects.

The shortage may well be real. But every organisation making the loudest noise about it earns money from your belief in it.

Quantum computing. For years, projections circulated that quantum would need two million workers by 2025, or 500,000 new hires by 2025.

Then somebody counted. QED-C, an industry body, measured the actual global quantum workforce at about 16,500 people in its 2026 report.

Not two million. Sixteen thousand five hundred.

The projection was not slightly optimistic. It was wrong by a factor of more than a hundred.

The lesson: separate counting from guessing

QED-C’s 16,500 is a count of people who exist. The two million was a guess about people who might exist one day.

Both get printed as numbers. Only one is a measurement.

Chapter 6 applies this specifically to claims about artificial intelligence, where the same confusion does the most damage.

The four questions

You now have a checklist. Use it on any number about jobs, including every number in this book.

1. Is there a range? A single number with no interval is a rumour.

2. Where does it actually come from? Follow the chain to the original measurement. Check its size and its date.

3. Who benefits if I believe this? Training companies, staffing firms and software vendors fund most shortage claims.

4. Is this a count or a projection? Counting is measuring. Projecting is guessing with a spreadsheet.

Four questions. Ten seconds each. They will save you years.

Chapter 5 gives you the ranking system this book uses to grade every source it touches.