Categories → Causal Wizard Concept , Data , Variables , Study Design
One individual or observation included in a sample is referred to as a "sample unit" or "observation unit," which represents the smallest entities within a sample that are studied and analyzed.
In statistics and experimental design, one individual entity included in a sample is commonly referred to as a "sample unit" or "observation unit." It refers to a single element or member of a sample group. The sample unit could be a person, an object, or an event, depending on the research question and design. Sample units are the smallest entities within a sample that are studied and analyzed, to draw conclusions about the larger population from which they were drawn.
Collectively, a sample refers to a subset of a population that is randomly selected for the purpose of collecting data and making inferences about the larger population. A sample is considered to be representative of the population if it accurately reflects the characteristics of the population, such as age, gender, and socioeconomic status.
Despite the definitions given above, it is common to see one individual participant or observation be referred to as a "Sample", when strictly speaking that individual is a Sample Unit. In your Datasets or data files, a Sample Unit would correspond to one row. The columns of the row contain the attributes or properties of that Sample Unit.
The process of selecting a sample involves the use of sampling techniques, such as simple random sampling, stratified sampling, or cluster sampling, to ensure that every individual in the population has an equal chance of being selected. This helps to minimize sampling bias, which occurs when certain groups in the population are over- or underrepresented in the sample.
Once a sample is selected, data can be collected through various methods, such as surveys, questionnaires, or experiments. The data collected from the sample can then be analyzed to make inferences about the population. This process involves the use of statistical tests and measures, such as hypothesis testing and confidence intervals, to determine the probability that the observed differences between the sample and population are due to chance or actual differences.
Overall, the use of individual samples is a key component of statistical and experimental design as it allows researchers to make inferences about larger populations while minimizing the potential for sampling bias.
It is important to distinguish whether the sample refers to one individual entity (i.e. one sample unit), or the set sampled from the population (a sample).