Q: What You decide you want your sample to be representative of the population. Which sampling process would provide the most representative sample?

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a stratified random sample

This is a non-random sampling technique where the initial respondents to a survey recruit others for a survey. Linear snowball sampling is when the first responent recruits one more person for the survey, who in turn recruits one more person for the survey. Number of people surveyed will increase in a linear manner. The related links provide good discussion of this form of surveying. It is a non-representative sample, but it is an effective means of surveying people with common traits who might otherwise be difficult to find. For example, I want a statistical profile of people that are "hackers" so every hacker I find, I would ask if I could interview his "fellow hackers." See related links.

The purpose of statistical inference is to obtain information about a population form information contained in a sample.

If I take 10 items (a small sample) from a population and calculate the standard deviation, then I take 100 items (larger sample), and calculate the standard deviation, how will my statistics change? The smaller sample could have a higher, lower or about equal the standard deviation of the larger sample. It's also possible that the smaller sample could be, by chance, closer to the standard deviation of the population. However, A properly taken larger sample will, in general, be a more reliable estimate of the standard deviation of the population than a smaller one. There are mathematical equations to show this, that in the long run, larger samples provide better estimates. This is generally but not always true. If your population is changing as you are collecting data, then a very large sample may not be representative as it takes time to collect.

The optimum sample size is based on a trade-off between the precision required for the estimate(s) and the cost of sampling. The precision required depends on the consequences of making the wrong decision. I would expect much higher precision for a medical trial than I would for a weather forecast.The necessary sample size, to attain that precision will depend on the characteristic that is being estimated (mean, variance, proportion), the underlying distribution and the test being used. Then there is the cost (money and time) that depend on the sample size.Since you have not bothered to share any information on any of these factors, I cannot provide a more useful answer.The optimum sample size is based on a trade-off between the precision required for the estimate(s) and the cost of sampling. The precision required depends on the consequences of making the wrong decision. I would expect much higher precision for a medical trial than I would for a weather forecast.The necessary sample size, to attain that precision will depend on the characteristic that is being estimated (mean, variance, proportion), the underlying distribution and the test being used. Then there is the cost (money and time) that depend on the sample size.Since you have not bothered to share any information on any of these factors, I cannot provide a more useful answer.The optimum sample size is based on a trade-off between the precision required for the estimate(s) and the cost of sampling. The precision required depends on the consequences of making the wrong decision. I would expect much higher precision for a medical trial than I would for a weather forecast.The necessary sample size, to attain that precision will depend on the characteristic that is being estimated (mean, variance, proportion), the underlying distribution and the test being used. Then there is the cost (money and time) that depend on the sample size.Since you have not bothered to share any information on any of these factors, I cannot provide a more useful answer.The optimum sample size is based on a trade-off between the precision required for the estimate(s) and the cost of sampling. The precision required depends on the consequences of making the wrong decision. I would expect much higher precision for a medical trial than I would for a weather forecast.The necessary sample size, to attain that precision will depend on the characteristic that is being estimated (mean, variance, proportion), the underlying distribution and the test being used. Then there is the cost (money and time) that depend on the sample size.Since you have not bothered to share any information on any of these factors, I cannot provide a more useful answer.

Related questions

a stratified random sample

Describe how more complex probability sampling techniques could provide samples more representative of a target population than simple random sampling Illustrate your answer with an information technology example.

a stratified random sample

a stratified random sample

when there are errors in sampling design, such as biases in selecting participants or a non-representative sample, which can lead to inaccurate results.

Stratified random sampling includes all subgroups within a population with numbers proportional to their presence in the overall population. This method ensures that each subgroup's representation in the sample reflects its true proportion in the larger population, helping to provide a more accurate and representative sample.

Random sampling prevents any emergent patterns inherent in a non-random selection process. Second, random samples of the population provide the widest representation of the population as a whole (given that the sample is large enough). This is why there is emphasis and the quantity of the study group-- the larger the population being analyzed the more accurate a representation it will be during the course of the study.

they must be correct and they must be the right kind of sample

Advantages of Poisson sampling method include its simplicity and ease of application, as well as its ability to provide unbiased estimates of population parameters. Disadvantages may include potential underrepresentation of rare events or small subgroups in the population, as well as the assumption of random and independent sampling.

When using a quadrat, it is important to ensure that an adequate number of sampling sites are selected to provide a representative sample of the area being studied. Precautions should be taken to ensure that sampling sites are randomly and evenly distributed to avoid bias. Additionally, it is important to consider the size of the quadrat relative to the size of the study area to ensure that it is appropriate for capturing the variation in the population being studied.

Survey sampling involves selecting a representative subset of the population, which can be more practical and cost-effective than surveying the entire population in a census. Proper sampling techniques can still provide accurate and reliable results, as long as the sample is chosen correctly and is representative of the population of interest.

Surveys and polls conducted using random sampling are generally considered the most reliable measure of public opinion. By ensuring a representative sample of the population is included, these methods provide insights into the attitudes and preferences of a broader audience.