Sampling theory is the study of relationships existing between a population and samples drawn from the population. It is of value in estimating of unknown population parameters (such as population arithmetic mean, variance, etc.) from knowledge of corresponding sample statistics. Sampling theory is also useful in determining whether observed differences between two samples are actually due to chance variation or whether they are really significant. The answers involve use of so-called tests of significance and hypotheses. In general, a study of inferences made concerning a population by use of samples drawn from it, together with indications of the accuracy of such inferences using probability theory, is called statistical inference.
A sample is a set of individuals or objects selected from a population. The purpose of sampling is to infer a characteristic or characteristics of a given population from a subset of measurements obtaining from the sample. In a quantitative research design, the sampling methods and the sample design are crucial to obtain unbiased and consistent estimates of the characteristic population inferred from the sample; and, enable one to statistically validate any conclusions based on the observations and findings. In order that conclusions of sampling theory and statistical inference be valid, samples must be chosen so as to be representative of the population. A study of methods of sampling and the related problems, which arise, is called the design of the experiment. One way in which a representative sample may be obtained is by a process of random sampling, according to which each member of the population has an equal chance of being included in the sample. One technique for obtaining a random sample is to assign a number to each member of the population, write each number on a separate piece of paper, place them in a container and then draw numbers from the container, being careful to mix thoroughly before each drawing. Alternatively, this can be replaced by using a table of random numbers specially constructed for such purposes.
How well a sample represents a given population depends on the sample frame, the sample size and the specific sample design:
A sample is a set of individuals or objects selected from a population. The purpose of sampling is to infer a characteristic or characteristics of a given population from a subset of measurements obtaining from the sample. In a quantitative research design, the sampling methods and the sample design are crucial to obtain unbiased and consistent estimates of the characteristic population inferred from the sample; and, enable one to statistically validate any conclusions based on the observations and findings. In order that conclusions of sampling theory and statistical inference be valid, samples must be chosen so as to be representative of the population. A study of methods of sampling and the related problems, which arise, is called the design of the experiment. One way in which a representative sample may be obtained is by a process of random sampling, according to which each member of the population has an equal chance of being included in the sample. One technique for obtaining a random sample is to assign a number to each member of the population, write each number on a separate piece of paper, place them in a container and then draw numbers from the container, being careful to mix thoroughly before each drawing. Alternatively, this can be replaced by using a table of random numbers specially constructed for such purposes.
How well a sample represents a given population depends on the sample frame, the sample size and the specific sample design:
- Sample Frame: the set of subjects who have a chance of being selected from the study population, given the sampling approach chosen
- Sample Design: the specific procedures to be used for selecting the subjects in the sample
- Sample Size: the planning of, and reasons for choosing, the number of subjects in the sample.
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good explanation
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