Four reasons why the polls were so wrong about the election

Sampling problems

Sampling issues relate to whether or not the surveys were actually representative of the wider electorate. The issue of sampling bias is complicated and it comes down to the difference between what are called “random samples” and “quota samples”. Most internet surveys use a form of quota sampling in which polling agencies try to replicate the characteristics of the U.S. electorate by including certain numbers of blacks, women, young people and so on. This approach can fail to include hard-to-reach groups such as older people not connected to the internet or those living in rural areas.

Random samples pre-select individuals using probability theory and so are more likely to be accurate, since everyone in the electorate has a chance, albeit very small, of being chosen for interview. But random sample surveys cost time and money, so they are not a feasible method for conducting last-minute polls.

Telephone polls do use a system of random digit dialling done by computer to identify potential respondents. Since this is a random sampling method it should, on the face of it, be more accurate than quota samples. The problem is that pollsters have to call many people before they can get someone willing to talk to them. Response rates can fall below 10 percent, invalidating the advantage of this method because those willing to talk are not representative of Americans in general. Overall, it is possible the final polls may have excluded Trump supporters if many of them were in hard-to-reach groups.