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2. Sample size determination - Wikipedia

   en.wikipedia.org/wiki/Sample_size_determination

   Sample size determination or estimation is the act of choosing the number of observations or replicates to include in a statistical sample. 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.

3. Acceptance sampling - Wikipedia

   en.wikipedia.org/wiki/Acceptance_sampling

   MIL-STD-105E was cancelled in 1995 but is available in related documents such as ANSI/ASQ Z1.4, "Sampling Procedures and Tables for Inspection by Attributes". Several levels of inspection are provided and can be indexed to several AQLs. The sample size is specified and the basis for acceptance or rejection (number of defects) is provided. MIL ...

4. Variables sampling plan - Wikipedia

   en.wikipedia.org/wiki/Variables_sampling_plan

   where ¯ and are the sample mean and the standard deviation respectively, is the critical distance, and are the upper and lower regulatory limits. When the above expression is satisfied the proportion nonconforming is lower than expected and therefore the lot is accepted.

5. Design effect - Wikipedia

   en.wikipedia.org/wiki/Design_effect

   Where is the sample size, = / is the fraction of the sample from the population, () is the (squared) finite population correction (FPC), is the unbiassed sample variance, and (¯) is some estimator of the variance of the mean under the sampling design. The issue with the above formula is that it is extremely rare to be able to directly estimate ...

6. Sampling (statistics) - Wikipedia

   en.wikipedia.org/wiki/Sampling_(statistics)

   A visual representation of the sampling process. In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population.

7. Oversampling and undersampling in data analysis - Wikipedia

   en.wikipedia.org/wiki/Oversampling_and_under...

   Overabundance of already collected data became an issue only in the "Big Data" era, and the reasons to use undersampling are mainly practical and related to resource costs. Specifically, while one needs a suitably large sample size to draw valid statistical conclusions, the data must be cleaned before it can be used. Cleansing typically ...