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In Random Sampling, each member of the population has an equal chance of being selected as subject. . It is also considered as a fair way of selecting a sample from a given population since every member is given equal opportunities of being selected. Another key feature of simple random sampling is its representativeness of the population.
Scope & Importance
There are many methods to proceed with simple random sampling. An unbiased random selection and a representative sample is important in drawing conclusions from the results of a study. Remember that one of the goals of research is to be able to make conclusions pertaining to the population from the results obtained from a sample. Due to the representativeness of a sample obtained by simple random sampling, it is reasonable to make generalizations from the results of the sample back to the population. The most primitive and mechanical would be the lottery method. Each member of the population is assigned a unique number. Each number is placed in a bowl or a hat and mixed thoroughly. The blind-folded researcher then picks numbered tags from the hat. All the individuals bearing the numbers picked by the researcher are the subjects for the study. Another way would be to let a computer do a random selection from your population. For populations with a small number of members, it is advisable to use the first method but if the population has many members, a computer-aided random selection is preferred.
One of the best things about simple random sampling is the ease of assembling the sample. Theoretically, the only thing that can compromise its representativeness is luck. If the sample is not representative of the population, the random variation is called sampling error.
Sampling done without replacement is no longer independent, but still satisfies exchangeability, hence many results still hold. Further, for a small sample from a large population, sampling without replacement is approximately the same as sampling with replacement, since the odds of choosing the same individual twice is low. In small populations and often in large ones, such sampling is typically done "without replacement", i.e., one deliberately avoids choosing any member of the population more than once. However, this does not guarantee that a particular sample is a perfect representation of the population. Simple random sampling merely allows one to draw externally valid conclusions about the entire population based on the sample. Although simple random sampling can be conducted with replacement instead, this is less common and would normally be described more fully as simple random sampling with replacement. An unbiased random selection of individuals is important so that if a large number of samples were drawn, the average sample would accurately represent the population.
The global analytical chemistry and instrumentation market was estimated to be $30.2 billion in the year 2011 and is expected to grow 8.4% from 2011 to 2016 to reach $45.2 billion. Spectrometry segment has the largest share (33.8%) followed by chromatography (22%) in the year 2011. The spectrometry market is driven by the coupling of mass spectrometry with chromatography techniques, growing 7.4% from 2011 to 2016.
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