Assumption 1) Weighting and sample frame
Polls work by choosing roughly 1,000 to 2,000 people and using their opinions to represent the broader UK public. But this is only accurate if your 2,000 people sample is representative of the country.
To do this, pollsters set quotas on the number of people from different age groups, genders, regions etc. to ensure that their sample roughly matches the population. Then, if any groups are under-represented or over-represented, they are weighted up or down to make sure the final results are accurate.
But we can't put a weight and quota on everything - doing so decreases the “weighting efficiency”, which indicates that the weights are introducing more bias to the results. So tradeoffs have to be made. Some pollsters weight on 2016 EU referendum vote, others weight on ethnicity, others weight on social class.
More in Common allocates quotas for age/sex interlocked, education level, and region. We also weight on 2019 General Election vote and ethnicity. We think that these demographic factors are likely to be the most predictive of voting intention at this election, and avoid recall issues from asking people to remember their 2016 vote.
Here’s an example of why sample frames matter. More in Common has always set specific quotas for 75+ year old voters, whereas other pollsters opt for a broader category of 65+ year olds. Given that 75+ year olds are more likely to vote, and more likely to vote Conservative, this has the effect of giving us a slightly higher Conservative vote share than we would have otherwise.
(There are good reasons for why other pollsters don’t recruit for 75+ year olds. For example, you could argue that lower internet uptake among this group means that 75+ year olds on online panels could not represent the wider 75+ age group).
When we ran this experiment in March, the result was a difference in Labour lead of 23 when we did not allocate for 75+ year olds, versus 17 points when we did, keeping all other methodology exactly the same.