### Some articles on *sample, sample size, size, samples*:

Bootstrapping (statistics) - Situations Where Bootstrapping Is Useful

... indirect method to assess the properties of the distribution underlying the

... indirect method to assess the properties of the distribution underlying the

**sample**and the parameters of interest that are derived from this distribution ... When the**sample size**is insufficient for straightforward statistical inference ... is well-known, bootstrapping provides a way to account for the distortions caused by the specific**sample**that may not be fully representative of the ...Estimation - Uncorrected Sample Standard Deviation

... of a finite population) can be applied to the

... of a finite population) can be applied to the

**sample**, using the**size**of the**sample**as the**size**of the population (though the actual population**size**from ... This estimator, denoted by sN, is known as the uncorrected**sample**standard deviation, or sometimes the standard deviation of the**sample**(considered as the entire population ... is a consistent estimator (it converges in probability to the population value as the number of**samples**goes to infinity), and is the maximum-likelihood estimate when the population is normally distributed ...Statewide Opinion Polling For The April, May, And June Democratic Party Presidential Primaries, 2008 - Puerto Rico

... won To be determined Poll source Date Highlights Vocero/Univision Puerto Rico

... won To be determined Poll source Date Highlights Vocero/Univision Puerto Rico

**Sample size**300LV Margin of error ± 3.4% May 8–20, 2008 Clinton 59%, Obama 40%, Undecided 1% El Vocero/Uni ...PASS

... PASS is a computer program for estimating

**Sample Size**Software... PASS is a computer program for estimating

**sample size**or determining the power of a statistical test or confidence interval ... PASS includes over 150 documented**sample size**and power procedures ...Algorithmic Inference - Inferring Functions With The Help of A Computer

... The drawback is that the central limit theorem is applicable when the

... The drawback is that the central limit theorem is applicable when the

**sample size**is sufficiently large ... Therefore it is less and less applicable with the**sample**involved in modern inference instances ... The fault is not in the**sample size**on its own part ...### Famous quotes containing the words size and/or sample:

“There are some persons we could not cut down to *size* without diminishing ourselves as well.”

—Jean Rostand (1894–1977)

“All that a city will ever allow you is an angle on it—an oblique, indirect *sample* of what it contains, or what passes through it; a point of view.”

—Peter Conrad (b. 1948)

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