Cable Forum - View Single Post

**Hugh** · 24-02-2021, 15:57

Quote:

Originally Posted by Pierre

study of only 1200 people and no link to the study in the article, I would suggest bollocks, especially when using % on such small sample sizes. 1no. SNP supporter asked = 100% either way etc.

---------- Post added at 14:17 ---------- Previous post was at 14:03 ----------

Ok thanks for that.

So the conclusions on how a Brexit party voter and SNP voter will view the vaccine is based on a sample size of 25 people and 48 people respectively........

1,855,175 people voted SNP at the last election and 951,372 voted Brexit Party

So it's a representative sample of 0.0025% for both, I wouldn't use it for my lottery numbers.

https://opentextbc.ca/researchmethod...cting-surveys/

Quote:

Sample Size and Population Size
Why is a sample of 1,000 considered to be adequate for most survey research—even when the population is much larger than that? Consider, for example, that a sample of only 1,000 registered voters is generally considered a good sample of the roughly 25 million registered voters in the Canadian population—even though it includes only about 0.00004% of the population! The answer is a bit surprising.

One part of the answer is that a statistic based on a larger sample will tend to be closer to the population value and that this can be characterized mathematically. Imagine, for example, that in a sample of registered voters, exactly 50% say they intend to vote for the incumbent. If there are 100 voters in this sample, then there is a 95% chance that the true percentage in the population is between 40 and 60. But if there are 1,000 voters in the sample, then there is a 95% chance that the true percentage in the population is between 47 and 53. Although this “95% confidence interval” continues to shrink as the sample size increases, it does so at a slower rate. For example, if there are 2,000 voters in the sample, then this reduction only reduces the 95% confidence interval to 48 to 52. In many situations, the small increase in confidence beyond a sample size of 1,000 is not considered to be worth the additional time, effort, and money.

Another part of the answer—and perhaps the more surprising part—is that confidence intervals depend only on the size of the sample and not on the size of the population. So a sample of 1,000 would produce a 95% confidence interval of 47 to 53 regardless of whether the population size was a hundred thousand, a million, or a hundred million.