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  2. Sample size determination - Wikipedia

    en.wikipedia.org/wiki/Sample_size_determination

    To determine an appropriate sample size n for estimating proportions, the equation below can be solved, where W represents the desired width of the confidence interval. The resulting sample size formula, is often applied with a conservative estimate of p (e.g., 0.5): = / for n, yielding the sample size Sample sizes for binomial proportions ...

  3. OpenEpi - Wikipedia

    en.wikipedia.org/wiki/OpenEpi

    Various confidence intervals for proportions, ... Sample size for proportions, ... Users can run the program in English, French, Spanish, Portuguese or Italian. ...

  4. Mark and recapture - Wikipedia

    en.wikipedia.org/wiki/Mark_and_recapture

    The Lincoln–Petersen estimator is asymptotically unbiased as sample size ... confidence interval for the population size N ... Open Source R programming ...

  5. Bootstrapping (statistics) - Wikipedia

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

    When working with small sample sizes (i.e., less than 50), the basic / reversed percentile and percentile confidence intervals for (for example) the variance statistic will be too narrow. So that with a sample of 20 points, 90% confidence interval will include the true variance only 78% of the time. [44]

  6. Z-test - Wikipedia

    en.wikipedia.org/wiki/Z-test

    Difference between Z-test and t-test: Z-test is used when sample size is large (n>50), or the population variance is known. t-test is used when sample size is small (n<50) and population variance is unknown. There is no universal constant at which the sample size is generally considered large enough to justify use of the plug-in test.

  7. Binomial proportion confidence interval - Wikipedia

    en.wikipedia.org/wiki/Binomial_proportion...

    In contrast, it is worth noting that other confidence interval may have coverage levels that are lower than the nominal , i.e., the normal approximation (or "standard") interval, Wilson interval, [8] Agresti–Coull interval, [13] etc., with a nominal coverage of 95% may in fact cover less than 95%, [4] even for large sample sizes.

  8. Confidence interval - Wikipedia

    en.wikipedia.org/wiki/Confidence_interval

    The confidence interval can be expressed in terms of probability with respect to a single theoretical (yet to be realized) sample: "There is a 95% probability that the 95% confidence interval calculated from a given future sample will cover the true value of the population parameter."

  9. PASS Sample Size Software - Wikipedia

    en.wikipedia.org/wiki/PASS_Sample_Size_Software

    PASS is a computer program for estimating sample size or determining the power of a statistical test or confidence interval. NCSS LLC is the company that produces PASS. NCSS LLC also produces NCSS (for statistical analysis). PASS includes over 920 documented sample size and power procedures.