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  2. 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. Typical ...

  3. Sample size determination - Wikipedia

    en.wikipedia.org/wiki/Sample_size_determination

    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 .

  4. Sampling distribution - Wikipedia

    en.wikipedia.org/wiki/Sampling_distribution

    In statistics, a sampling distribution or finite-sample distribution is the probability distribution of a given random-sample-based statistic.If an arbitrarily large number of samples, each involving multiple observations (data points), were separately used to compute one value of a statistic (such as, for example, the sample mean or sample variance) for each sample, then the sampling ...

  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. Fisher consistency - Wikipedia

    en.wikipedia.org/wiki/Fisher_consistency

    Suppose our sample is obtained from a finite population Z 1, ..., Z m. We can represent our sample of size n in terms of the proportion of the sample n i / n taking on each value in the population. Writing our estimator of θ as T(n 1 / n, ..., n m / n), the population analogue of the estimator is T(p 1, ..., p m), where p i = P(X = Z i).

  7. Sampling probability - Wikipedia

    en.wikipedia.org/wiki/Sampling_probability

    Generally, the first-order inclusion probability of the ith element of the population is denoted by the symbol π i and the second-order inclusion probability that a pair consisting of the ith and jth element of the population that is sampled is included in a sample during the drawing of a single sample is denoted by π ij. [3]

  8. Binomial distribution - Wikipedia

    en.wikipedia.org/wiki/Binomial_distribution

    This fact is the basis of a hypothesis test, a "proportion z-test", for the value of p using x/n, the sample proportion and estimator of p, in a common test statistic. [35] For example, suppose one randomly samples n people out of a large population and ask them whether they agree with a certain statement. The proportion of people who agree ...

  9. Margin of error - Wikipedia

    en.wikipedia.org/wiki/Margin_of_error

    6 Effect of finite population size. 7 See also. 8 References. ... (left), and sample size ... choosing a correct formula for ...