What Is a Sample?

Because it can be time-consuming and costly to stud🥀y la🌱rge groups, marketers, scientists, governments, and other researchers use smaller subsets or samples to make decisions that affect the larger population.

Researchers and statisticians employ sampling techniques, such as probability or random sampling, where subjects are chosen at r🎶andom, and non-probability sampling, where samples are delibe✨rately selected.

Simple Random Sample

A sample is an unbiased number of observations taken from a population. For an unbiased sample, the selection must be random so that all individuals in the population have an equal chance of being added to the group. Simple random sampling is ideal if every entity in the population is identical.

Systematic Sample

Systematic sampling is a form of probability sampling. Similar to simple random sampling, it involves selecting random samples in fixed periodic intervals. Reಌsearchers calculate the interva🅘l by dividing the total population by the required sample size.

Unlike simple random sampling, 澳洲幸运5官方开奖结果体彩网:systematic sampling is more efficient. There is also a lower risk of data being manipulated. This type is best used when there is some order in the population, when the population is large and known, especially when time and resources are limi♈ted, and when the sample is evenly spread across the population.

Stratified Random Sample

Also referred to as proportional random sampling or quota random sampling, a 澳洲幸运5官方开奖结果体彩网:stratified random sample is better than a simple random sample. This type of sampling divides the overall population into smaller groups known as strata. People within the strata share similar characteristics.

A stratified random sampling technique can create layers or strata by age group. For example, two participants, Alex and David, are 22 and 24 years old, respectively. They are two of many CFA exam test takers. The sample selection cannot pick one over the other based on some prefere♛ntial mechanism. They both should have an equal chance of bei෴ng selected from their age group.

The table is divided into age groups. Suppose 30,000 people within the age range of 20 to 24 years old took the CFA exam in 2024. Using this same proportion, the sample group will have (30,000 ÷ 200,000) × 1,000 = 150 test-takers that fall within this group. Alex or David—or both or neither—may be included among the 150 random exam participants of the sample.

Cluster Sample

Cluster sampling is a form of random sampling. Clusters are defined as different subsets of the larger population. Individual sa🔯mples within the cluster have similar characteristics. Cluster sampling is commonly used when there are large populations that are spread out, making it expensive and time-consuming to study each subject.

To create this sample, researchers first define groups and then define samples within these clusters. There are disadvantages to cluster sampling, as researchers may be biased when they pick their clusters and samples. As such, the samples may not accurately represent the population at large.

Population Considerations

A population is the total number of observations contained in a given group or context. The total number of people in any given country is the population. The total number of students in a city can be considered a population, and the total number of dogs in a city is also a population size. A sample is a portion, part, or fraction of the whole group and acts as a subset of that population. Sampl൩es are used in settings wh❀ere research is conducted.

Researchers cannot gain access to entire populations, so samples are used. Using a smaller group to represent the population can still produce valid results while reducing time and resources. However, samples must resemble the broader population to make accurate inferences or predictions. All the participants in the sample should share the same characteristics and qualities.

If the study concerns male college freshmen, the sample should be a small percentage of males who fit this description. Similarly, if a research group conducts studies on the sleep patterns of single women over 50, the sample should only include women within this demographic.

The Bottom Line

Sampling can help researchers, government entities, and businesses analyze large populations. It is a cost-effective way for researchers to study group🧸s while saving time.