# What is Sampling?

## Sampling

Sampling refers to the process of selecting a subset (or a sample) from a larger population or set for the purpose of estimating or drawing conclusions about the entire population. This method is commonly used in statistics, research, quality control, and auditing, among other fields, because it is often impractical or impossible to examine the entire population.

There are two primary types of sampling:

1. Probability Sampling: Every member of the population has a known, non-zero chance of being selected in the sample. Types include:
• Simple Random Sampling: Every member and combination of members has an equal chance of being selected.
• Stratified Sampling: The population is divided into subgroups (strata) based on a specific characteristic, and then samples are randomly selected from each stratum.
• Cluster Sampling: The entire population is divided into clusters (groups), a random sample of clusters is chosen, and all members within those selected clusters are surveyed.
• Systematic Sampling: Every nth item from the list of population is selected.
2. Non-Probability Sampling: Not all members of the population have a known or equal chance of being included in the sample. Types include:
• Convenience Sampling: Selecting the easiest-to-reach or most convenient population members.
• Judgmental or Purposive Sampling: The researcher selects specific members of the population based on their knowledge or judgment.
• Snowball Sampling: Existing study participants recruit future participants among their acquaintances.
• Quota Sampling: The researcher gathers a specific number of subjects from various subgroups based on predefined criteria.

• Reduces time and cost compared to studying the entire population.
• Can achieve a high level of accuracy if done correctly.
• Facilitates the study of large populations where a census might be impractical.

• Potential for sampling errors.
• Results might not be as accurate as studying the entire population (though in some cases, a well-conducted sample can be more accurate than a poorly-conducted census).
• Biases can be introduced based on the method of sample selection.

Sampling is a foundational concept in many fields, and its proper execution is critical for obtaining valid and reliable results.

## Example of Sampling

Let’s use an example from the context of market research:

Scenario: A company that produces organic fruit juices wants to launch a new flavor. Before doing a full-scale production, they decide to do market research to find out if consumers would like the new flavor.

Population: The company sells its products nationwide. So, the entire population is all the adults in the country, which is, let’s say, 50 million adults.

Sampling: It’s impractical and costly to ask all 50 million adults for their opinion. Instead, the company decides to use a sampling method.

Sampling Method Chosen: Stratified Sampling

Reason: The company believes that preferences might vary based on age groups. They decide to divide the population into three strata based on age:

• Young adults (18-30 years)
• Middle-aged (31-50 years)
• Seniors (51 and above)

Process:

• From each age stratum, the company randomly selects 1,000 individuals, resulting in a total sample size of 3,000 respondents.
• These selected individuals are then given a sample of the new flavor and are asked for their feedback.

Results:

• 700 young adults liked the new flavor.
• 600 middle-aged respondents liked it.
• 550 seniors liked it.

Based on the sample, 1,850 out of 3,000 respondents liked the new flavor, which is 61.7%.

Conclusion: If the sample is representative of the broader population, approximately 61.7% of the national population might like the new flavor. The company may decide to launch the product based on this feedback, expecting a favorable reception from the majority.

Note: The company should be cautious. Even though sampling provides good estimates, there’s still a margin of error. The true percentage of the entire population that likes the new flavor might be a bit lower or higher than 61.7%. Properly designed and executed sampling techniques, however, can keep this margin of error minimal.