No, what I am saying is that, like literally any other numerical measurement obtained by scientific means (and it is here when I can say, with absolute confidence, that I know what I am talking about!), the central value is not the only thing you should care about. Of equal, or in fact greater, important, is the margin of error attached to that figure. In general, whenever a scientist (physics, biology, political science, whatever -- it's all the same thing) quotes something as being of a certain size, eg giving the number 48, what they really mean is something along the lines of "there is approximately a 70% chance that the measurement is between 44 and 51, and a 95% chance that it's between 41 and 55". Ideally, the range should be smaller, but it is always the case that this range exists, and it's therefore always the case that the central value isn't in itself important. Unfortunately, the central value is all anyone outside the field (ie the media, politicians and the wider public) seem to care about. So they say "Hllary Clinton is going to get 47% of the vote", and are then shocked when she gets 45, even though this was well within the 70% range.
You don't need to be a polling expert to know this. Any basic course in statistics covers it for you. And yet, most people seem to lack this basic statistical knowledge, are horrified or thrilled when a prediction is "wrong", and call the entire field into question based on very superficial reasons.
The central values for polling were in the end wrong for Tuesdays election. However, the final prediction by Nate Silver (mentioned above) gave Clinton around a 70% chance of winning, and Trump a 30% chance. 30% events aren't all that rare, and were well-captured by the polling and modelling in the end.
SO, in short, Katie Hopkins is wrong as usual.