Most methods consider sampling only from a homogeneous population in which each animal has the same probability of becoming infected.
With simple random sampling, every possible sample of size n has the same probability of being selected.
In a random sample of a class of 50 students, for example, each student has the same probability, 1/50, of being selected.
Assuming that each attack scenario has the same probability for occurrence, optimal system designs that account for the simulated possible attack scenarios are obtained.
Tables of random numbers, or computer-generated random numbers, can be used to guarantee that each element has the same probability of being selected.
Choosing the elements from the population one at a time so that each element has the same probability of being selected will provide a simple random sample.
In games of pure chance, each instance is a completely independent one; that is, each play has the same probability as each of the others of producing a given outcome.
For a population of size N, a simple random sample is a sample selected such that each possible sample of size n has the same probability of being selected.
In this paper, we study the lifetime of a large scale sensor network with n randomly deployed sensors communicating with a base station (BS), where each sensor node has the same probability to sense and report its data to the BS.
The latter has the same probability of success as the legal method.
In our case, each component has the same probability to be selected.
