Search results
Results from the WOW.Com Content Network
Nonprobability sampling methods include convenience sampling, quota sampling, and purposive sampling. In addition, nonresponse effects may turn any probability design into a nonprobability design if the characteristics of nonresponse are not well understood, since nonresponse effectively modifies each element's probability of being sampled.
The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power. In complex studies ...
Judgment sampling or purposive sampling, where the researcher chooses the sample based on who they think would be appropriate for the study. This is used primarily when there is a limited number of people that have expertise in the area being researched, or when the interest of the research is on a specific field or a small group.
The purposive approach (sometimes referred to as purposivism, [1] purposive construction, [2] purposive interpretation, [3] or the modern principle in construction) [4] is an approach to statutory and constitutional interpretation under which common law courts interpret an enactment (a statute, part of a statute, or a clause of a constitution) within the context of the law's purpose.
This is particularly important for purposive surveys, but can also be used to guide sampling surveys by eliminating the need to survey areas where, for geological or other reasons, we can reasonably expect all ancient traces to be destroyed (e.g., by erosion) or far too deeply buried (e.g., by alluvium) to be detectable.
In statistics, a sampling distribution or finite-sample distribution is the probability distribution of a given random-sample-based statistic.For an arbitrarily large number of samples where each sample, involving multiple observations (data points), is separately used to compute one value of a statistic (for example, the sample mean or sample variance) per sample, the sampling distribution is ...
Importance sampling is a variance reduction technique that can be used in the Monte Carlo method.The idea behind importance sampling is that certain values of the input random variables in a simulation have more impact on the parameter being estimated than others.
This page was last edited on 28 April 2009, at 20:53 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may ...