Sampling (statistics)

Lore & Background

The concept of random sampling by using lots is an old idea, mentioned several times in the Bible. His estimates used Bayes' theorem with a uniform prior probability and assumed that his sample was random. Alexander Ivanovich Chuprov introduced sample surveys to Imperial Russia in the 1870s. More than two million people responded, with names obtained through magazine subscription lists and telephone directories, which were heavily biased towards Republicans, making the sample deeply flawed despite its large size. According to the Elections Department, sample counts help reduce speculation and misinformation while helping election officials check against the election result. The reported sample counts yield a fairly accurate indicative result with a 4% margin of error at a 95% confidence interval, but are separate from official results.

Reader's Guide

Sampling is essential in business and medical research for gathering information about a population. It has lower costs and faster data collection compared to a census, and can provide insights when measuring an entire population is impossible, such as getting sizes of all stars in the universe. Successful practice depends on focused problem definition, including defining the population from which the sample is drawn. A sampling frame—a list of elements with contact information—is often used to enable probability sampling, where every unit has a known chance of selection. This allows unbiased estimates of population totals by weighting sampled units according to their probability of selection. Acceptance sampling is used to determine if a production lot meets governing specifications.

Did You Know?

• Random sampling by using lots is mentioned several times in the Bible.