## Politics, Polls and Insignificance

Posted in Bad Statistics, Politics with tags , , , , , on July 29, 2014 by telescoper

In between various tasks I had a look at the news and saw a story about opinion polls that encouraged me to make another quick contribution to my bad statistics folder.

The piece concerned (in the Independent) includes the following statement:

A ComRes survey for The Independent shows that the Conservatives have dropped to 27 per cent, their lowest in a poll for this newspaper since the 2010 election. The party is down three points on last month, while Labour, now on 33 per cent, is up one point. Ukip is down one point to 17 per cent, with the Liberal Democrats up one point to eight per cent and the Green Party up two points to seven per cent.

The link added to ComRes is mine; the full survey can be found here. Unfortunately, the report, as is sadly almost always the case in surveys of this kind, neglects any mention of the statistical uncertainty in the poll. In fact the last point is based on a telephone poll of a sample of just 1001 respondents. Suppose the fraction of the population having the intention to vote for a particular party is $p$. For a sample of size $n$ with $x$ respondents indicating that they hen one can straightforwardly estimate $p \simeq x/n$. So far so good, as long as there is no bias induced by the form of the question asked nor in the selection of the sample, which for a telephone poll is doubtful.

AÂ  little bit of mathematics involving the binomial distribution yields an answer for the uncertainty in this estimate of p in terms of the sampling error:

$\sigma = \sqrt{\frac{p(1-p)}{n}}$

For the sample size given, and a value $p \simeq 0.33$ this amounts to a standard error of about 1.5%. About 95% of samples drawn from a population in which the true fraction is $p$ will yield an estimate within $p \pm 2\sigma$, i.e. within about 3% of the true figure. In other words the typical variation between two samples drawn from the same underlying population is about 3%.

If you don’t believe my calculation then you could use ComRes’ own “margin of error calculator“. The UK electorate as of 2012 numbered 46,353,900 and a sample size of 1001 returns a margin of error of 3.1%. This figure is not quoted in the report however.

Looking at the figures quoted in the report will tell you that all of the changes reported since last month’s poll are within the sampling uncertainty and are therefore consistent with no change at all in underlying voting intentions over this period.

A summary of the report posted elsewhere states:

A ComRes survey for the Independent shows that Labour have jumped one point to 33 per cent in opinion ratings, with the Conservatives dropping to 27 per cent – their lowest support since the 2010 election.

No! There’s no evidence of support for Labour having “jumped one point”, even if you could describe such a marginal change as a “jump” in the first place.

Statistical illiteracy is as widespread amongst politicians as it is amongst journalists, but the fact that silly reports like this are commonplace doesn’t make them any less annoying. After all, the idea of sampling uncertainty isn’t all that difficult to understand. Is it?

And with so many more important things going on in the world that deserve better press coverage than they are getting, why does a “quality” newspaper waste its valuable column inches on this sort of twaddle?

## Uncertain Attitudes

Posted in Bad Statistics, Politics with tags , , , , on May 28, 2014 by telescoper

It’s been a while since I posted anything in the bad statistics file, but an article in today’s Grauniad has now given me an opportunity to rectify that omission.
The piece concerned, entitled Racism on the rise in Britain is based on some new data from the British Social Attitudes survey; the full report can be found here (PDF). The main result is shown in this graph:

The version of this plot shown in the Guardian piece has the smoothed long-term trend (the blue curve, based on a five-year moving average of the data and clearly generally downward since 1986) removed.

In any case the report, as is sadly almost always the case in surveys of this kind, neglects any mention of the statistical uncertainty in the survey. In fact the last point is based on a sample of 2149 respondents. Suppose the fraction of the population describing themselves as having some prejudice is $p$. For a sample of size $n$ with $x$ respondents indicating that they describe themselves as “very prejudiced or a little prejudiced” then one can straightforwardly estimate $p \simeq x/n$. So far so good, as long as there is no bias induced by the form of the question asked nor in the selection of the sample…

However, a little bit of mathematics involving the binomial distribution yields an answer for the uncertainty in this estimate of p in terms of the sampling error:

$\sigma = \sqrt{\frac{p(1-p)}{n}}$

For the sample size given, and a value $p \simeq 0.35$ this amounts to a standard error of about 1%. About 95% of samples drawn from a population in which the true fraction is $p$ will yield an estimate within $p \pm 2\sigma$, i.e. within about 2% of the true figure. This is consistent with the “noise” on the unsmoothed curve and it shows that the year-on-year variation shown in the unsmoothed graph is largely attributable to sampling uncertainty; note that the sample sizes vary from year to year too. The results for 2012 and 2013 are 26% and 30% exactly, which differ by 4% and are therefore explicable solely in terms of sampling fluctuations.

I don’t know whether racial prejudice is on the rise in the UK or not, nor even how accurately such attitudes are measured by such surveys in the first place, but there’s no evidence in these data of any significant change over the past year. Given the behaviour of the smoothed data however, there is evidence that in the very long term the fraction of population identifying themselves as prejudiced is actually falling.

Newspapers however rarely let proper statistics get in the way of a good story, even to the extent of removing evidence that contradicts their own prejudice.