Sampling data should be done very carefully. Systematic sampling is frequently chosen because it is a simple method. Then choose every fiftieth name thereafter until you have a total of 400 names (you might have to go back to the beginning of your phone list). Number the population 1–20,000 and then use a simple random sample to pick a number that represents the first name in the sample.

You must choose 400 names for the sample. Your phone book contains 20,000 residence listings.

For example, suppose you have to do a phone survey. To choose a systematic sample, randomly select a starting point and take every n th piece of data from a listing of the population. All members of the four departments with those numbers are the cluster sample. Number each department, and then choose four different numbers using simple random sampling. Divide your college faculty by department. For example, if you randomly sample four departments from your college population, the four departments make up the cluster sample. All the members from these clusters are in the cluster sample. To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. Besides herself, Lisa’s group will consist of Marcierz, Cuningham, and Cuarismo. The two-digit number 14 corresponds to Macierz, 05 corresponds to Cuningham, and 04 corresponds to Cuarismo. However the third random number, 0.14669, contains 14 (the fourth random number also contains 14), the fifth random number contains 05, and the seventh random number contains 04. The random numbers 0.94360 and 0.99832 do not contain appropriate two digit numbers. If she needed to, Lisa could have generated more random numbers. Each random number may only contribute one class member. Lisa reads two-digit groups until she has chosen three class members (that is, she reads 0.94360 as the groups 94, 43, 36, 60). For this example, suppose Lisa chooses to generate random numbers from a calculator. Lisa can use a table of random numbers (found in many statistics books and mathematical handbooks), a calculator, or a computer to generate random numbers. A more technological way is for Lisa to first list the last names of the members of her class together with a two-digit number, as in the figure below: Figure 1.7: Lisa’s Class Roster ID To choose a simple random sample of size three from the other members of her class, Lisa could put all 31 names in a hat, shake the hat, close her eyes, and pick out three names. In other words, each sample of the same size has an equal chance of being selected.įor example, suppose Lisa wants to form a four-person study group (herself and three other people) from her pre-calculus class, which has 31 members not including Lisa. Any group of n individuals is equally likely to be chosen as any other group of n individuals if the simple random sampling technique is used. The gold standard and maybe easiest method to describe is called a simple random sample (SRS). In each form, each member of a population initially has an equal chance of being selected for the sample. There are several different methods of random sampling. This section will describe a few of the most common methods. Statisticians use various methods of random sampling in an attempt to achieve this goal. Instead, we typically use a sample of the population which should have the same characteristics as the population it is representing. Gathering information about an entire population often costs too much or is virtually impossible.

Example of simple random sampling