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  2. Sample size determination - Wikipedia

    en.wikipedia.org/wiki/Sample_size_determination

    For B = 10% one requires n = 100, for B = 5% one needs n = 400, for B = 3% the requirement approximates to n = 1000, while for B = 1% a sample size of n = 10000 is required. These numbers are quoted often in news reports of opinion polls and other sample surveys. However, the results reported may not be the exact value as numbers are preferably ...

  3. eBay - Wikipedia

    en.wikipedia.org/wiki/EBay

    eBay office in Toronto, Canada. eBay Inc. (/ ˈ iː b eɪ / EE-bay, often stylized as ebay or Ebay) is an American multinational e-commerce company based in San Jose, California, that allows users to buy or view items via retail sales through online marketplaces and websites in 190 markets worldwide.

  4. Sub-sampling (chemistry) - Wikipedia

    en.wikipedia.org/wiki/Sub-sampling_(chemistry)

    A riffle box is a box containing a number (between 3 and 12) of "chutes" - slotted paths through which particles of the sample may slide. The sample is dropped into the top, and the box produces two equally divided subsamples. Riffle boxes are commonly used in mining to reduce the size of crushed rock samples prior to assaying.

  5. Stratified sampling - Wikipedia

    en.wikipedia.org/wiki/Stratified_sampling

    Proportionate allocation uses a sampling fraction in each of the strata that are proportional to that of the total population. For instance, if the population consists of n total individuals, m of which are male and f female (and where m + f = n), then the relative size of the two samples (x 1 = m/n males, x 2 = f/n females) should reflect this proportion.

  6. Bootstrapping (statistics) - Wikipedia

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

    The simplest bootstrap method involves taking the original data set of heights, and, using a computer, sampling from it to form a new sample (called a 'resample' or bootstrap sample) that is also of size N. The bootstrap sample is taken from the original by using sampling with replacement (e.g. we might 'resample' 5 times from [1,2,3,4,5] and ...

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  8. Behrens–Fisher problem - Wikipedia

    en.wikipedia.org/wiki/Behrens–Fisher_problem

    In statistics, the Behrens–Fisher problem, named after Walter-Ulrich Behrens and Ronald Fisher, is the problem of interval estimation and hypothesis testing concerning the difference between the means of two normally distributed populations when the variances of the two populations are not assumed to be equal, based on two independent samples.

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