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# advantages of simple random sampling

From Wikipedia

Random sample

A sample is a subject chosen from a population for investigation. A random sample is one chosen by a method involving an unpredictable component. Random sampling can also refer to taking a number of independent observations from the same probability distribution, without involving any real population. The sample usually is not a representative of the population from which it was drawn&mdash; this random variation in the results is known as sampling error. In the case of random samples, mathematical theory is available to assess the sampling error. Thus, estimates obtained from random samples can be accompanied by measures of the uncertainty associated with the estimate. This can take the form of a standard error, or if the sample is large enough for the central limit theorem to take effect, confidence intervals may be calculated.

## Types of random sample

• A simple random sample is hi selected so that all samples of the same size have an equal chance of being selected from the population.
• A self-weighting sample, also known as an EPSEM (Equal Probability of Selection Method) sample, is one in which every individual, or object, in the population of interest has an equal opportunity of being selected for the sample. Simple random samples are self-weighting.
• Stratified sampling involves selecting independent samples from a number of subpopulations, group or strata within the population. Great gains in efficiency are sometimes possible from judicious stratification.
• Cluster sampling involves selecting the sample units in groups. For example, a sample of telephone calls may be collected by first taking a collection of telephone lines and collecting all the calls on the sampled lines. The analysis of cluster samples must take into account the intra-cluster correlation which reflects the fact that units in the same cluster are likely to be more similar than two units picked at random.

## Methods of producing random samples

From Encyclopedia

simple random sampling Dictionary definition of simple random ...

simple random sample

Question:And can you please explain the situation which can lead to the method of systematic sampling being biased. WIll choose a best answer. Thanks In Advance

Question:At a large university a simple random sample of 5 female professors is selected and a simple random sample of 10 male professors is selected. The two samples are combined to give an overall sample of 15 professors. Te overall sample is 1.a simple random sample 2.biased 3.a stratified sample 4.none of the above To select a sample of undergraduate students in the United States, I select a simple random sample of four states. From each of these states, I select a simple random sample of two colleges or universities. Finally, form each of these eight colleges or universities; I select a simple random sample of 20 undergraduates. My final sample consists of160 undergraduates. This is an example of 1.simple random sampling 2.stratified random sampling 3.multistage sampling 4. convenience sampling

Answers:The first is an example of: 3. a stratified sample. The second is an example of: 3. multistage sampling.

Question:

Answers:when there is a lot production, it is difficult and cumbersome to inspect and test all the items of the lot so random acceptence sampling is followed for these lots for inspection. Advantge is that you can test any say 10 items of the 100 and have a feel of the lot !

Question:

Answers:When sub-populations vary considerably, it is advantageous to sample each subpopulation (stratum) independently. Stratification is the process of grouping members of the population into relatively homogeneous subgroups before sampling. The strata should be mutually exclusive: every element in the population must be assigned to only one stratum. The strata should also be collectively exhaustive: no population element can be excluded. Then random or systematic sampling is applied within each stratum. This often improves the representativeness of the sample by reducing sampling error. It can produce a weighted mean that has less variability than the arithmetic mean of a simple random sample of the population. Advantages over other sampling methods Focuses on important subpopulations and ignores irrelevant ones. Allows use of different sampling techniques for different subpopulations. Improves the accuracy/efficiency of estimation. Permits greater balancing of statistical power of tests of differences between strata by sampling equal numbers from strata varying widely in size.