In classical theory the measurements that are made on the sampling units in the population are usually assumed to follow a frequency distribution, i.e. the normal distribution, of known mathematical form apart from certain population parameters such as the mean and variance whose values have to be estimated from the sample data. In sample survey theory, on the other hand, the attitude has been to assume only very limited information about this frequency distribution. In particular, its mathematical form is not assumed known, so that the approach might be described as model-free or distribution-free. This attitude is natural for large surveys in which many different measurements with differing frequency distributions are made on the units. In surveys in which only a few measurements per unit are made, studies of their frequency distribution may justify the assumption of known mathematical forms, permitting the results from classical theory to be applied.

El muestreo es una técnica de investigación científica, su función es determinar que parte de una realidad en estudio debe estudiarse con la capacidad de crear inferencias  sobre dicha población.

Madji, is there any extra information you can provide? There are many branches in sampling theory. Do you refer to SUS (stochastic universal sampling technique) or sampling in general?

Greeting :

yes, I mean sampling in general. And thank you all for your  considerations. 

Stochastic Universal Sampling Technique ?

Stochastic universal sampling (SUS) is a technique used in genetic algorithms for selecting potentially useful solutions for recombination. It was introduced by James Baker.[1]

SUS is a development of fitness proportionate selection (FPS) which exhibits no bias and minimal spread. Where FPS chooses several solutions from the population by repeated random sampling, SUS uses a single random value to sample all of the solutions by choosing them at evenly spaced intervals. This gives weaker members of the population (according to their fitness) a chance to be chosen and thus reduces the unfair nature of fitness-proportional selection methods.

FPS can have bad performance when a member of the population has a really large fitness in comparison with other members. Using a comb-like ruler, SUS starts from a small random number, and chooses the next candidates from the rest of population remaining, not allowing the fittest members to saturate the candidate space.

For any of the functions of the testing suite, the bias B is quite near to zero when Stochastic Universal Sampling is used. This indicates that there almost do not exist differences between the expected number of offspring for each individual and the effective sampling frequencies.

Also a reasonable genetic diversity is preserved even at the final stages with values of b ranging between 0.6 to 0.8. Regarding to Proportional Selection most of the cases show a value of B near to 0.8. This is an expected result due to the limited population size. In this case the genetic diversity is low with values of b ranging between 0.8 to 1.

Hello

Stochastic universal sampling (SUS) is a technique used in genetic algorithms for selecting potentially useful solutions . It was introduced by James Baker.