What are the sampling methods?

In a statistical study, sampling methods refer to how we select the members of the population to be included in the study.

If a sample is not selected randomly, it will probably be somehow distorted and the data may not be representative of the population.

There are many ways to select a sample: some good and some bad.

Sample of convenience: The researcher chooses a sample that is readily available in some non-random way.

Example: A researcher takes a survey of people while they are walking down the street.

Because it is probably biased: The place and time of day and other factors can produce a sample of biased people.

Voluntary response sample: The researcher asks members of a population to participate in the sample and the people decide whether or not they are part of the sample.

Example: A TV show host asks his viewers to visit his website and respond to an online survey.

Because he is probably biased: People who take the time to respond tend to have opinions as strong as the rest of the population.

PRACTICAL PROBLEM 1

A restaurant leaves comment cards on all its tables and encourages customers to participate in a short survey to learn about their overall experience.

Good ways to taste

Simple random sample: Each member and group of members has the same chance to be included in the sample. Technology, random number generators, or some other kind of random process is necessary to obtain a simple random sample.

Example: Teachers put students’ names in a hat and choose without trying to get a sample of students.

Because it’s good: Random samples are usually quite representative because they don’t favor certain members.

Stratified random sample: The population is first divided into groups. The overall sample consists of a few members of each group. The members of each group are chosen at random.

Example – A student council investigates 100100100 students by obtaining random samples of 252525 freshmen, 25252525 second-year students, 25252525 second-year students, 25252525 junior students and 25252525 senior students.

Because it’s good: A stratified sample ensures that the members of each group will be represented in the sample, so this sampling method is good when we want some members of each group.

Random cluster sample: The population is first divided into groups. The overall sample consists of each member of certain groups. The groups are selected at random.

Example: An airline wants to survey its customers one day, then randomly select 555 flights that day and survey each passenger on those flights.

Because it’s good: A cluster sample gets each member from some of the groups, so it’s good when each group reflects the population as a whole.

Systematic random sample: Members of the population are put in a certain order. A starting point is selected at random, and each n^{\text{n

n, start superscript, start text, t, h, end text, end member superscript is selected to be in the sample.

Example-A the headmaster takes an alphabetical list of student names and chooses a random starting point. Every 20th{text}20

20, start superscript, start text, t, h, end text, end student superscript is selected to take a poll.