What is a 'Representative Sample'
A representative sample is a small quantity of something that accurately reflects the larger entity. An example is when a small number of people accurately reflect the members of an entire population. In a classroom of 30 students, in which half the students are male and half are female, a representative sample might include six students: three males and three females.
BREAKING DOWN 'Representative Sample'
When a sample is not representative, the result is known as a sampling error. Using the classroom example again, a sample that includes six students, all of whom are male, would not be a representative sample.
A representative sample parallels the key variables and characteristics under examination. Some examples include sex, age, education level, socioeconomic status or marital status. Using a larger sample size increases the likelihood that the sample more accurately reflects what actually exists in the population. Any information collection with biased tendencies is unable to generate a representative sample.
Reasons to Use a Representative Sample
A representative sample allows the collected results to be generalized to a larger population. For most marketing or psychology studies, it is impractical in terms of time, finances and effort to collect data on every person in the target population. This is especially impractical for large population such as an entire country or race.
Risks of Using Samples
The use of sample groups poses risks, as the sample may not accurately reflect the views of the general population. One of the largest risks is developing a sample that is not truly representative. This most likely occurs because the population group is too small. For example, when comparing data relating to gender, a representative sample must include individuals of different ages, economic status and geographical locations. Such information typically requires a diversification of information-collecting sites.
Random Sampling and Purposive Sampling
Random sampling involves choosing respondents from the target population at random, to minimize bias in a representative sample. While this method is more expensive and requires more upfront information, the information yielded is typically of higher quality. Purposive sampling is more widely used, and occurs when the managers target individuals matching certain criteria for information extraction. Ideal interview candidates receive profiles. Although this leads to the potential of bias in the representative sample, the information is easier to collect, and the sampler has more control when creating the representative sample.
True Representative Samples Cannot Exist
When developing a survey, the manager must utilize controls to track and monitor who has provided input, whether the information is usable, and whether it can be interpreted. Random sampling ensures every member of the population has equal probability of selection and inclusion in the sample group. However, sample bias is always present and can never truly be eliminated. For example, individuals who are too busy to participate will be under-represented in the representative sample, as they are less likely to provide feedback.
What is a 'Simple Random Sample'
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. An example of a simple random sample would be the names of 25 employees being chosen out of a hat from a company of 250 employees. In this case, the population is all 250 employees, and the sample is random because each employee has an equal chance of being chosen.
BREAKING DOWN 'Simple Random Sample'Researchers can create a simple random sample using a couple of methods. With a lottery method, each member of the population is assigned a number, after which numbers are selected at random. The example in which the names of 25 employees out of 250 are chosen out of a hat is an example of the lottery method at work. Each of the 250 employees would be assigned a number between 1 and 250, after which 25 of those numbers would be chosen at random.
For larger populations, a manual lottery method can be quite onerous. Selecting a random sample from a large population usually requires a computer-generated process, by which the same methodology as the lottery method is used, only the number assignments and subsequent selections are performed by computers, not humans.
Simple Random Sample Advantages
Ease of use represents the biggest advantage of simple random sampling. Unlike more complicated sampling methods such as stratified random sampling and probability sampling, no need exists to divide the population into subpopulations or take any other additional steps before selecting members of the population at random.
A simple random sample is meant to be an unbiased representation of a group. It is considered a fair way to select a sample from a larger population, since every member of the population has an equal chance of getting selected.
Simple Random Sample Disadvantages
A sampling error can occur with a simple random sample if the sample does not end up accurately reflecting the population it is supposed to represent. For example, in our simple random sample of 25 employees, it would be possible to draw 25 men even if the population consisted of 125 women and 125 men. For this reason, simple random sampling is more commonly used when the researcher knows little about the population. If the researcher knew more, it would be better to use a different sampling technique, such as stratified random sampling, which helps to account for the differences within the population, such as age, race or gender.