How can a sample of only 800 or 1200 truly reflect the opinions of 200 million Americans within a few percentage points?
Sampling methods and measures of sample reliability or precision are derived from a mathematical science called statistics. Statistics is a subject taught in colleges and some high schools. Text books on the subject are available in most libraries. At the root of statistical reliability is probability; i.e., the odds of obtaining a particular outcome by chance alone. As an example, the chances of having a coin come up heads in a single toss is 50%. Heads is one of only two possible outcomes. The chance of getting two heads in two coin tosses is less because two heads are now only one of four possible outcomes; i.e., a head/head, head/tail, tail/head and tail/tail. As the number of coin tosses increases, it becomes increasingly more likely to get outcomes which are either very close to half heads or exactly half because, as with two coins, there are more ways to get such outcomes. Sample survey reliability works the same way – but on a much larger scale. As in coin tosses, the most like
