DON’T KILL THE audience

WHY JOURNALISTS NEED DATA –AND data needs journalists

statsitisA terrible new diseases – statsitis – affects 1 in 1,000 people. It is always fatal. Thankfully, it’s treatable.

The treatment always cures you if you’ve got statsitis. But if you haven’t and take the treatment, it’ll make you REALLY ill.

There is a test, 95% accurate, that will tell you if you have it.

You take the test. You test positive. Who wants treatment? What’s the chance you’ve got statsitis?

USE AND ABUSE OF STATS

REALLY?How much does a slice of bread weigh?

…what about a pizza base?

…what about cheese?

…meat?

…tomato?

use and abuse of stats

REALLY?INCOME: £2,000 / month

OUTGOINGS: £2,050 / month (including rent, debt, etc)

DEFICIT: = £2,050 - £2,000 = £50 / month

DEBT: £20,000

I cancel my £20 / month gym membership. Do I need to cut another £30, or £19,800?

use and abuse of stats

REALLY?MEDIAN FULL-TIME INCOME (UK): £26,000

TAX ON £26,000: £3,905

A significant % of people don’t work, or work part time.

So how, exactly, are we paying £4,000 each for pensions?

USE AND ABUSE OF STATS

REALLY?Highest ever TV audience: 1.1bn

Actual (est) wedding audience: 300m

....we all do it sometimes.

REMEMBER THIS?A terrible new diseases – statsitis – affects 1 in 1,000 people. It is always fatal. Thankfully, it’s treatable.

The treatment always cures you if you’ve got statsitis. But if you haven’t and take the treatment, it’ll make you REALLY ill.

There is a test, 95% accurate, that will tell you if you have it.

You take the test. You test positive. Who wants treatment? What’s the chance you’ve got statsitis?

Here comes the science partA 95% accurate test will get it wrong 5 times out of 100.

So for 1,000 people, 5% x 1,000 = 50 will test positive. Of those, only 1 is ill.

So if test positive, you have only a 1 in 50 = 2% chance of having the illness.

What does that mean in real life?

Who else gets this wrong?The example just given was based on a real study performed on a group of professionals.

The first time this was carried out (Eddy, 1982), 95% of professionals over-estimated by a factor of ten

They thought a positive result meant a 75% chance of having the illness. In reality, it was 7.7%

They were all doctors.