Example Scenario Not A Prediction

An incredible projection. This graph was used by the UK’s pair of chief coronavirus government advisors on 21 September.

‘Look! Second Wave Incoming!’

It caused mayhem.

Cases were rising in the country, they said. Do you think a ‘case’ is a positive test for infection or when around four of every five infected are asymptomatic, someone actually being sick?

Get over that conundrum if you can.

The chart displayed, was all part of the scientists’ – those with no concern for economic health let’s not forget – plan to push for a devastating (and many claim, not just the Swedes, wildly counterproductive) second lockdown.

Laughably, their accompanying spiel was, ‘this is not a prediction’.

And you thought politicians had no shame.

The stats, if you can truly call them that, were that if positive test results “continued” to double every seven days, then look at the catastrophe we’d be at in just a few weeks.

Now do as we say.

As so many a commentator, reporter and interested member of the at-risk public immediately realised, these figures were wholly misleading.

One more example of the famed riposte, ‘there’s lies, damned lies, and statistics’.

Yet here, deliberately shown for alarm.

One week one, how is that non-prediction faring?


Here’s a close-up.

Turns out, a classic FUD move.

Sow fear, uncertainty and doubt.

It must have felt so clever at the time.

As the top scientists in the land sat around, discussing how to tighten their hand on the lever of coronapower. Wondering how they could instil the most frightening of horror into the population. Get them ready for more quarantine and cement their cherished suppression strategy as the only show in town.

Then they produce an “example scenario” that fails to stand up to the slightest of scrutiny.

Why choose the doubling time of seven days? Why no weighting for positive test percentage? What impact do age stratas have? Where are new hospital admission numbers?

In the hours following broadcast, I tried to find a piece defending their forecast.

I could not find a single one.

Not even a sole voice, mainstream or niche, supporting this graph.

Which must tell you something.

I naturally wondered how this tactic could be deployed in the heat of selling battle.

Imagine certain prospect issues that you can readily quantify. Declining market share. Productivity. Margin pressure.

You could well fairly easily find an ally for whom drafting a nightmare ‘example scenario’ would be a dream.

But what if you then end up in the same spot as these scientists?

Receiving brickbats all round, trouncing your numbers, irreparably damaging your flawed judgement.

Well, at least people are talking about the issue on your terms. So maybe not all bad.

Yet my advice has always been the same on business case development.

Three simple rules;

Get the raw data from prompting your prospect.

Choose the least-worst case, avoiding the end-of-days view completely, as your platform.

Show sensitivity bands (think fan charts), separately, as a taster for your estimates (“E”), (which also teases out political allegiances).

Then you should avoid being labelled, as this pair were with a play on their surnames, witless and unbalanced.

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