Random Sampling Method

1.Simple Random Sampling:

Simple random sampling is the most widely-used probability sampling method, probably because it is easy to implement and easy to analyze.Simple random sampling refers to a sampling method that has the following properties.

• The population consists of N objects.

• The sample consists of n objects.

• All possible samples of n objects are equally likely to occur.

An important benefit of simple random sampling is that it allows researchers to use statistical methods to analyze sample results. For example, given a simple random sample, researchers can use statistical methods to define a confidence interval around a sample mean. Statistical analysis is not appropriate when non-random sampling methods are used.The first

In other words, there are many usage models (same structure, different transition probabilities) that are consistent with what is known about the environment of use.Objective functions are used to choose the model that satisfies all constraints and is optimal relative to some criterion. By a combination of additional management constraints and objective functions the resulting model can emphasize aspects of the system or of the testing process that are important to testers.One must always be wary of constructing an overly complex model that might be ill-conditioned relative to the numerical methods used in calculating the solution. Too many constraints that are functions of long run behavior are not advised.A statistical analyst must be involved in this kind of modeling.Recent theoretical developments hold promise for dynamic revision of probabilities as testing progresses in order to optimize sampling relative to an importance objective.

Test Automation:

Usage models have led to increased test automation in almost every situation in which they have been used. Test automation is attractive because it vastly increases the number of tests that can be run and greatly reduces the unit cost of testing. Test automation is more cost effectively done when planned as a companion to the system development, but can also be cost effective for existing systems for which

It also ensures at the same time that each unit has equal probability of inclusion in the sample. In this method of sampling, the first unit is selected with the help of random numbers and the remaining units are selected automatically according to a predetermined pattern. Systematic sampling relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list.

Some precautionary notes concerning use of systematic sampling: 1.Periodicity in the population list (a) Problem: Variability of the measurement in the list such that comparable measurements appear at fixed intervals. Particular problems arise when the periodic intervals of similarity are integral multiples of the sampling interval k. (b) Remedy: Change the random start several times as one moves through the list during the selection process.

2.Monotonicity in the population list (a) Problem: Measurement values tend to increase or decrease as one moves through the list. The value of the random start therefore strongly influences the value of the estimate resulting from the