: The main objective in sampling is to select a sample from a population in order to estimate some unknown population parameter, usually a total or a mean of some interesting variable. When the units in the population do not have the same probabilities of being included in a sample, the sampling is called unequal probability sampling The inclusion probabilities are usually chosen to be proportional to some auxiliary variable that is known for all units in the population. When unequal probability sampling is applicable, it generally gives much better estimates than sampling with equal probabilities. A random sample is selected according to some specified random mechanism called the sampling design. For unequal probability sampling there exist many different sampling designs such as Poisson, conditi A sampling design which is obtained without replacement and the inclusion probabilities are proportional to the size of an auxiliary variable, is called a ampling onal Poisson, Sampford, Pareto and splitting sampling. The choice of sampling design is important since it determines the properties of the estimator that is. The comparison of different designs, is a problem in sampling. One of the measure is entropy, which i measurement for the level of randomization of the design. In general a sampling design should also have a high level of randomization. A design called adjusted conditional Poisson has maximum entropy, but using different populations, it has been shown that several designs are close in terms of entropy. A few designs yield low entropy and should therefore in general be avoided. In order to compare different designs it is also possible to look at some measure for the distance between designs. One such measure is the Hellinger distance can also be used. Some of the designs with high entropy are being compared and it had been shown that they have probability functions close to each other.