# What Is Sampling

What is sampling? What are the different methods of sampling?

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Sampling is a method of studying from a few selected items,instead of the entire big number of units. The small selection is called sample. The large number of items of units of particular characteristic is called population. Example: We check a sample of rice to see whether the rice well boiled or not. We check a small sample of solution to decide how much a given solution is concentrated. Thus with the sample we infer about a population. Some of the types of sampling are (1) simple random sampling. Mostly used for the type of population which is homogeneous.(2) Stratified sampling. Stratas help us classify the population when the population is heterogeneous and take simple random samples from each classes. (3) Sequential sampling is don by selection of the samples sequentially at regular intervals. The purpose of all the sampling techniques is to give the equal chance of any item to be selected without bias.

Sampling refers to the statistical process of selecting and studying the characteristics of a relatively small number of items from a relatively large population of such items,, to draw statistically valid inferences about the characteristics about the entire population.

There are two broad methods of sampling used by researchers, nonrandom (or judgment) sampling and random (or probability) sampling. In judgement sampling the researcher selects items to be drawn from the population based on his or her judgement about how well these items represent the whole population.The sample is thus based on someones knowledge about the population and the characteristics of individual items within it. The chance of an item being included in the sample are influenced by the characteristic of the item as judged by an expert selecting the item. A judgement sampling system is simple and less expensive to use. Also when there is very little known about the population under study a pilot study based on judgement sample is carried out to permit design of a more rigorous sampling system for a detailed study.

In random sampling, individual judgement plays no part in selection of sample. Each item in the sample stands equal chance of being included in the sample. In case of random sampling, the researcher is required to use specific statistical processes to ensure this equal probability of every item in the population. A random sampling system enables more reliable results of statistical analysis with measurable margins of errors and degree of confidence.

To improve the cost effectiveness of data collection and analysis, several variations of the random sampling are used by researchers. Some of the most common types of random sampling methods are (1) simple random sampling, (2) systematic sampling, stratified sampling, and (4) cluster sampling.

Simple random sampling ensures that each possible sample has an equal probability of being selected, and each item in the entire population has an equal chance of being included in the sample.

In systematic sampling the items are selected from the population at a uniform interval defined in terms of time, order or space. For example an observation may be made every half an hour, or from a long queue of people every fourth person may be selected, or in a bunch of documents every tenth document may be selected.

In stratified sample the entire population is divided in relatively homogeneous group. For example all the students of a school may be divided in groups of boy and girls. Once this is done random sample from each of such groups is drawn independently. This approach is suitable when there ate identifiable sub-groups exist within the population that differ significantly in respect of characteristic under study.

In cluster sampling the population is divided into groups or clusters, a sample of these clusters may be drawn. For example, a city may be divided in a cluster of small localities, and a sample of these localities may be drawn using random sampling methods. The all the households within each of the locality may be studied for the research. A research based on a well designed cluster sampling can often give better result than a research based on simple random sample with same time and cost of research.