The margin of error is *the* most widely misunderstood and misleading concept in statistics. It’s positively frightening to people who actually understand what it means to see how it’s commonly used in the media, in conversation, sometimes even by other scientists!

The basic idea of it is very simple. Most of the time when we’re doing statistics, we’re doing statistics based on a sample – that is, the entire population we’re interested in is difficult to study; so what we try to do is pick a *representative subset* called a sample. If the subset is *truly* representative, then the statistics you generate using information gathered from the sample will be *the same* as information gathered from the population as a whole.

But life is never simple. We *never* have perfectly representative samples; in fact, it’s *impossible* to select a perfectly representative sample. So we do our best to pick good samples, and we use probability theory to work out a predication of how confident we can be that the statistics from our sample are representative of the entire population. That’s basically what the margin of error represents: how well we *think* that the selected sample will allow us to predict things about the entire population.

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