Representative sample

A representative sample is a tiny amount or a subset of something larger, according to the definition. It exhibits the same characteristics and ratios as a bigger population.

Consider a company that is getting ready to introduce a new product in a US city, for instance. It will be nearly hard to send a survey to everyone in the city in order to get their opinions on the features of the product. To handle their comments on the product, researchers gather a small sample of people who will adequately represent the population of the city. We refer to this sample as a representative sample.

Researchers can generalize the knowledge they have gathered to a larger population when they use a representative sample. In terms of time, money, and resources, the majority of psychological and market research studies are inappropriate for gathering data on everyone. In particular, with a big population like a whole country, it is almost impossible to collect data from every single person.

Importance of a representative sample for studies in applied research

• A representative sample will be helpful to you if you want to conduct market research that is effective.
• A representative sample is a small group of individuals who as closely as possible represent a larger group. Then, using a sample of the population that we believe to be the most representative of our target group, we can conduct an online survey, for instance.

How to build a representative sample

1. Probability sampling: This technique involves selecting a sample from a broader population using a procedure based on probability theory. Participant selection must be done at random for them to be regarded as a probability sample.
2. Non-probability sampling is a sampling method in which the researcher chooses samples based on his or her personal assessment rather than through random selection. In contrast to probability sampling, which gives every member of the population a known chance of being chosen, non-probability sampling gives only some population members a chance of taking part in the study.

When a sample is not representative, we have a sampling error known as the margin of error. If we want a representative sample of 100 employees, we must select an equal number of men and women. For example, if we have a sample that is biased toward a specific genre, we will have a sample error.

The sample size is important, but it does not ensure that it accurately represents the population that we require. More than size, representativeness is linked to the sampling frame, or the list from which people are drawn, as in a survey. As a result, we must take care to include people from our target audience in that list in order to claim that it is a representative sample.