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  2. Standard normal table - Wikipedia

    en.wikipedia.org/wiki/Standard_normal_table

    Example: To find 0.69, one would look down the rows to find 0.6 and then across the columns to 0.09 which would yield a probability of 0.25490 for a cumulative from mean table or 0.75490 from a cumulative table. To find a negative value such as -0.83, one could use a cumulative table for negative z-values [3] which yield a probability of 0.20327.

  3. 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.

  4. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    About 68% of values drawn from a normal distribution are within one standard deviation σ from the mean; about 95% of the values lie within two standard deviations; and about 99.7% are within three standard deviations. [8] This fact is known as the 68–95–99.7 (empirical) rule, or the 3-sigma rule.

  5. 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 ...

  6. Standard score - Wikipedia

    en.wikipedia.org/wiki/Standard_score

    Comparison of the various grading methods in a normal distribution, including: standard deviations, cumulative percentages, percentile equivalents, z-scores, T-scores. In statistics, the standard score is the number of standard deviations by which the value of a raw score (i.e., an observed value or data point) is above or below the mean value of what is being observed or measured.

  7. Central limit theorem - Wikipedia

    en.wikipedia.org/wiki/Central_limit_theorem

    Let S n be the sum of n random variables. Many central limit theorems provide conditions such that S n / √ Var(S n) converges in distribution to N(0,1) (the normal distribution with mean 0, variance 1) as n → ∞. In some cases, it is possible to find a constant σ 2 and function f(n) such that S n /(σ √ n⋅f (n)) converges in ...

  8. The 4% rule creator says the opposite - AOL

    www.aol.com/finance/suze-orman-says-4-retirement...

    Here’s what you need to know to figure out what withdrawal strategy works best for you. Orman's alternative to the 4% rule. The money maven says she would “not be using the 4% rule on any ...

  9. Berry–Esseen theorem - Wikipedia

    en.wikipedia.org/wiki/Berry–Esseen_theorem

    In the case of independent samples, the convergence rate is n −1/2, where n is the sample size, and the constant is estimated in terms of the third absolute normalized moment. It is also possible to give non-uniform bounds which become more strict for more extreme events.