SAMPLING TECHNIQUE AND ITS DIFFERENT TYPES

ABOUT SAMPLING TECHINQUE

1. Sampling and its Technique

2. SAMPLING Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students. In statistics, sampling allows you to test a hypothesis about the characteristics of a population . The process of selecting a sample population from the target population is called the “sampling method ”. Use of various sampling techniques play a very important role in reducing cost, improving accuracy, creating more scope and achieving greater speed. The very important thing to be taken into consideration is that the sampling population should coincide with the target population .

3. 1. Simple Random Sampling A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. A simple random sample is meant to be an unbiased representation of a group . Example: An example of a simple random sample would be to choose the names of 25 employees from a company of 250 employees. At a birthday party, teams for a game are chosen by putting everyone's name into a jar, and then choosing the names at random for each team. A ssign each student a number from 1-100, place the numbers in a hat, then choose 30 numbers from the hat to be in your sample.

4. 2. Systematic Sampling Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – collecting data in an ordered or regular way. Systematic sampling helps minimize biased samples and poor survey results. for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling . I n a population of 10,000 people, a statistician selects every 100th person for sampling. The sampling intervals can also be systematic, such as choosing a new sample to draw from every 12 hours.

5. 3. S tratified Sampling In stratified sampling, researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).   In a study of stroke outcomes, we may stratify the population by sex, to ensure equal representation of men and women  those 100 customers would be divided into strata based on age, income, or other characteristics . divide a sample of adults into subgroups by age, like 18–29, 30–39, 40–49, 50–59, and 60 and above.

6. Cluster sampling is a survey sampling method wherein the population is divided into clusters, from which researchers randomly select some to form the sample.  This approach falls under the broader category of probability sampling, making it a valuable tool for examining extensive populations. EXAMPLE consumption of soda in a particular city, you could use area sampling to divide the city into different areas, called clusters, and then select certain clusters to be a part of the sample group . A health department wants to investigate the spread of a disease in a particular city.