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Sampling theory is a part of statistics that involves collection, analysis and interpretation of data collected from random samples of a population. It involves the uses of probability theory. To analyze the data from a random sample and draw conclusions there from a prior knowledge about the population parameters is required. In developing the theoretical background of sampling theory, the normal distribution as well as related probability distribution is used. It is applied only to random samples. Sampling theory is a study of the relationships between the universe and samples drawn from it. Basically, there are two advantages of sampling: first, it involves less cost and second, data can be collected faster than measuring the entire population. The process of sampling given in Sampling Theory Assignment Help are
• Defining concerned population.
• Specifying a set of items or events to be measured.
• Specifying a method of sampling for selection of items or events.
• Determining the size of sample.
• Execution of sampling plan.
• Collecting data and sampling. It can be better understood by Sampling Theory Assignment Help.
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Methods of Sampling
The different methods of sampling explained by Sampling Theory Assignment Help are
• Simple Random Sampling- In simple random sample, all the subsets of the frame are to be given an equal probability. It minimizes bias and simplifies the result. Because of its randomness, it is exposed to sampling error. It can become complex and tedious if sampling is done from a large target population.
• Systematic Sampling- It can also be called interval sampling. In this, the study population is arranged in a particular ordering scheme and then elements are selected at regular intervals from that list. It is started randomly and from then onwards every kth element is selected where k=size of population / size of sample. It should be kept in mind that the first sample in the list is not the starting point; instead it should be chosen randomly from the entire sample.
• Stratified Sampling- This method is applied when the population consists of different category. These categories are organized into separate ‘strata’. Each stratum is considered as an independent sub population and from this individual elements are selected randomly.
• Quota Sampling- In this, first the population is divided into mutually exclusive subgroup, like in stratified sampling. Then the subjects from each segment are selected on the basis of a specified proportion. Better Examples are given under Sampling Theory Assignment Help.
• Minimax Sampling- It is applied in imbalance data sets. The data set is resampled in a conservative manner called minimax sampling.
• Panel Sampling- In this method firstly, participants are selected randomly and then they are asked for information several times over a period of time. Each period of the collection of data is called a ‘wave’.
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