disadvantages of simple random sampling
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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— 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
- Random number table
- Mathematical algorithms for pseudo-random number generators
- Physical randomization devices such as coins, playing cards or sophisticated devices such as ERNIE
In statistics, a simple random sample is a subset of individuals (a sample) ...
simple random sample
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Answers:The first is an example of: 3. a stratified sample. The second is an example of: 3. multistage sampling.
Answers:A simple random sample is a specific kind of random sample. A random sample uses randomization to pick your sample. That can be done in a number of ways. A simple random sample is basically like selecting names from a hat. Every subject in the population has an equally likely chance of being in the sample, and every possible sample has an equally likely chance of being selected.. Other possible ways to take a random sample are systematic sampling (where you randomly select a starting point, and then pick every nth subject), cluster sampling (where subjects are separated into clusters, then all of the subjects in a random sample of clusters are put in the sample), and stratified sampling (where subjects are put into strata, and then a random sample is taken from each strata). Non random samples do not use randomization. Online polls are an example of a nonrandom sample. The person taking the sample does not take an effort to pick the sample; people choose themselves to be in the sample. Your example is a simple random sample. It's just like drawing from a hat. In short, simple random samples are random samples, but random samples are not necessarily simple random samples.
Answers:ANSWER: Random Sampling vs. Simple Random Sampling is the two have negligibly different definitions. They're the same thing. Random Sampling is the practice concerned with the selection of individuals intended to yield "some knowledge" Simple Random Sampling is the practice of selecting individuals entirely by chance such that each individual is chosen from a larger set of a population.
Answers:You need some strong result on the error in the standard variance estimator. I know only of bounds on this. If you asked about the sample *mean* that would be much easier. Maybe you could explore it with computer simulation? Is it really independent of the population variance?