Communicating Uncertainty: How to better understand an estimate‪.‬ Statistically Speaking

The ONS podcast returns, this time looking at the importance of communicating uncertainty in statistics. Joining host Miles Fletcher to discuss is Sir Robert Chote, Chair of the UKSA; Dr Craig McLaren, of the ONS; and Professor Mairi Spowage, director of the Fraser of Allander Institute. 

 

Transcript 

MILES FLETCHER 

Welcome back to Statistically Speaking, the official podcast of the UK’s Office for National Statistics. I'm Miles Fletcher and to kick off this brand new season we're going to venture boldly into the world of uncertainty. Now, it is of course the case that nearly all important statistics are in fact estimates. They may be based on huge datasets calculated with the most robust methodologies, but at the end of the day they are statistical judgments subject to some degree of uncertainty. So, how should statisticians best communicate that uncertainty while still maintaining trust in the statistics themselves? It's a hot topic right now and to help us understand it, we have another cast of key players. I'm joined by the chair of the UK Statistics Authority Sir Robert Chote, Dr. Craig McLaren, head of national accounts and GDP here at the ONS, and from Scotland by Professor Mairi Spowage, director of the renowned Fraser of Allander Institute at the University of Strathclyde. Welcome to you all.  

Well, Sir Robert, somebody once famously said that decimal points in GDP is an economist’s way of showing they've got a sense of humour. And well, that's quite amusing - particularly if you're not an economist - there's an important truth in there isn't there? When we say GDP has gone up by 0.6%. We really mean that's our best estimate. 

  
SIR ROBERT CHOTE 
It is. I mean, I've come at this having been a consumer of economic statistics for 30 years in different ways. I started out as a journalist on the Independent and the Financial Times writing about the new numbers as they were published each day, and then I had 10 years using them as an economic and fiscal forecaster. So I come at this very much from the spirit of a consumer and am now obviously delighted to be working with producers as well. And you're always I think, conscious in those roles of the uncertainty that lies around particular economic estimates. Now, there are some numbers that are published, they are published once, and you are conscious that that's the number that stays there. But there is uncertainty about how accurately that is reflecting the real world position and that's naturally the case. You then have the world of in particular, the national accounts, which are numbers, where you have initial estimates that the producer returns to and updates as the information sets that you have available to draw your conclusions develops over time. And it's very important to remember on the national accounts that that's not a bug, that's a feature of the system. And what you're trying to do is to measure a very complicated set of transactions you're trying to do in three ways, measuring what the economy produces, measuring incomes, measuring expenditure. You do that in different ways with information that flows in at different times. So it's a complex task and necessarily the picture evolves. So I think from the perspective of a user, it's important to be aware of the uncertainty and it's important when you're presenting and publishing statistics to help people engage with that, because if you are making decisions based on statistics, if you're simply trying to gain an understanding of what's going on in the economy or society, generally speaking you shouldn't be betting the farm on the assumption that any particular number is, as you say, going to be right to decimal places. And the more that producers can do to help people engage with that in an informed and intelligent way, and therefore mean that decisions that people take on the basis of this more informed the better.  

  
MF 
So it needs to be near e