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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. List of semiconductor scale examples - Wikipedia

    en.wikipedia.org/wiki/List_of_semiconductor...

    MOSFET (PMOS and NMOS) demonstrations ; Date Channel length Oxide thickness [1] MOSFET logic Researcher(s) Organization Ref; June 1960: 20,000 nm: 100 nm: PMOS: Mohamed M. Atalla, Dawon Kahng

  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. Subsampling - Wikipedia

    en.wikipedia.org/wiki/Subsampling

    Subsampling or sub-sampling may refer to: Sampling (statistics) Replication (statistics) Downsampling in signal processing; Chroma subsampling; Sub-sampling (chemistry)

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  7. p-chart - Wikipedia

    en.wikipedia.org/wiki/P-chart

    If δ is the size of the shift to detect, then the sample size should be set to () ¯ (¯). [ 2 ] : 278 Another technique is to choose the sample size large enough so that the p-chart has a positive lower control limit or n > 3 2 ( 1 − p ¯ ) p ¯ {\displaystyle n>{\frac {3^{2}(1-{\bar {p}})}{\bar {p}}}} .

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    AOL Mail welcomes Verizon customers to our safe and delightful email experience!

  9. Design effect - Wikipedia

    en.wikipedia.org/wiki/Design_effect

    A related form are weights normalized to sum to sample size (n). These (non-negative) weights sum to the sample size (n), and their mean is 1. Any set of weights can be normalized to sample size by dividing each weight with the average of all weights.