1. University of Southampton
The Polls in 2017
Will Jennings
University of Southampton
@drjennings

2. Outline Historical trends in campaign polls in Britain
Are polling errors getting bigger (everywhere)?
What errors, biases and groupthink should we beware in 2017?

3. The timeline of elections…
How do polls lines up with the election result over the campaign?
Calculate mean absolute error ( |Vote - Poll| ) of the daily poll-of-polls, for each day of the election timeline, for all major parties*, across all elections, from 1945 to 2015.
Focus on the last 50 days…
* Excludes UKIP, as we only have poll data for election cycle.

4. The timeline of elections…

5. Labour Party,

6. Conservative Party,

7. Are polling errors getting bigger?
Analysis of data for 13 countries 1960s to 2010s
Australia, Canada, France, Germany, Ireland, Netherlands, New Zealand, Norway, Portugal, Spain, UK, US, Denmark.
Mean absolute error of the daily poll-of-polls, for all parties/candidates for every day of final week of election campaign.

8. Are polling errors getting bigger?

9. Are polling errors getting bigger?

10. Are polling errors getting bigger?
Analysis of 8 national elections in :
Greece, Spain, Denmark, Ireland, Iceland, US, Canada, Australia.
Average mean absolute error of all final polls for the main parties (i.e. including smaller parties reduces MAE due to sampling error being a function of the vote share – and even this comparison is not perfect).

11. Polling errors worldwide, 2015-16
Typical mean absolute error across countries; all polls in the final week before the election (poll-of-polls where multiple polls), based on data from Jennings & Wlezien (2015).
Tells us how far, on average, the pre-election polls are out from the final result (139 elections in 23 countries).
1,353 party*election*days: all parties, MAE = 1.8.
753 party*election*days: ‘large parties’ (vote share > 20%), MAE = 2.3.
600 party*election*days – for ‘small parties’ (vote share < 20%), MAE = 1.2.
2.3 = the average MAE of polls for ‘large parties’ (>20% vote share) in elections in 23 countries (from Jennings & Wlezien ).
2.7 = the average MAE of polls in this set of elections
* In multi-party systems where polls for >2 parties overlap, average MAE is for 3 parties.

12. Anchoring bias? Shaping narratives
Is what really matters getting the story right?
Polls regularly showed Leave ahead (should really have been seen as a coin-flip?)
National polls in 2016 US presidential election were less wrong than 2012.
In knife-edge elections, polling error can be small but get the result wrong. How communicate that?

13. Expectation bias?
Ahead of the EU referendum, much discussion of ‘status quo bias’ of past votes.
After the 2015 election polling miss, random probability (‘gold standard’) surveys noted as getting closer to the final result. Over course of referendum campaign, random probability surveys (by NatCen and others) pointed to a Remain win. Did this influence expectations?

14. Summary History advises caution.
But polling errors aren’t getting worse, so still are valuable.
Extent/variety of methodological adjustment requires caution
Need to beware conventional wisdom, over- correction, hyping of results and new political alignments…