Chapter 9: THE MATHEMATICAL WORLD:修正案[9-31][9-32][9-33]:「標本抽出」の項目




下記はKahjinさんの訳文(31, 32, 33)への部分的な見直し案です.英語表現の解釈や文意の取り方について少しちがう見解をもったので参考用につくりました.










Most of what we learn about the world is obtained from information based on samples of what we are studying—samples of, say, rock formations, light from stars, television viewers, cancer patients, whales, or numbers. Samples are used because it may be impossible, impractical, or too costly to examine all of something, and because a sample often is sufficient for most purposes.

In drawing conclusions about all of something from samples of it, two major concerns must be taken into account. First, we must be alert to possible bias created by how the sample was selected. Common sources of bias in drawing samples include convenience (for example, interviewing only one's friends or picking up only surface rocks), self-selection (for example, studying only people who volunteer or who return questionnaires), failure to include those who have dropped out along the way (for example, testing only students who stay in school or only patients who stick with a course of therapy), and deciding to use only the data that support our preconceptions.

A second major concern that determines the usefulness of a sample is its size. If sampling is done without bias in the method, then the larger the sample is, the more likely it is to represent the whole accurately. This is because the larger a sample is, the smaller the effects of purely random variations are likely to be on its summary characteristics. The chance of drawing a wrong conclusion shrinks as the sample size increases. For example, for samples chosen at random, finding that 600 out of a sample of 1,000 have a certain feature is much stronger evidence that a majority of the population from which it was drawn have that feature than finding that 6 out of a sample of 10 (or even 9 out of the 10) have it. On the other hand, the actual size of the total population from which a sample is drawn has little effect on the accuracy of sample results. A random sample of 1,000 would have about the same margin of error whether it were drawn from a population of 10,000 or from a similar population of 100 million.