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Which sample size out of a population of 1,000 is most likely to lead to a valid conclusion50150200100?
Leia Leongson
China?
AlêxMP
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Which sample size out of a population of 1000 is most likely to lead to a valid conclusion?
Providing of course that a sample is representative of the population from which it is drawn, the bigger it is the more likely it will be to lead to a valid conclusion. Therefore, the best sample size when there are no restrictions, as in this case, would be one of 1000.
What are the disadvantages of non probability sampling?
It is quite likely that the sample is not representative of the population and so while statistical conclusion may be valid for the sample, they may not apply to the population.
Is a biased sample always valid?
a biased sample is valid determin
Why is it important to have a representative sample when doing surveys?
Because without representative sample, your results will not be valid.
Why would a company use a sample?
If it's too time consuming, expensive or otherwise impractical to collect data from the entire population of interest you can derive equally valid results from a random representative sample of adequate size. This saves time, money and other resources.
Which sample size out of a population of 1000 is most likely to lead to a valid conclusion?
Providing of course that a sample is representative of the population from which it is drawn, the bigger it is the more likely it will be to lead to a valid conclusion. Therefore, the best sample size when there are no restrictions, as in this case, would be one of 1000.
What are the disadvantages of non probability sampling?
It is quite likely that the sample is not representative of the population and so while statistical conclusion may be valid for the sample, they may not apply to the population.
Which conditions may be met for a scientifically valid sample?
at random to represent the population
Why is obtaining a representative sample important?
Many statistical statements for a population which are based on a sample are not valid if the sample is not representative.
What does it mean to say that the sample variance provides an unbiased estimate of the population variance?
It means you can take a measure of the variance of the sample and expect that result to be consistent for the entire population, and the sample is a valid representation for/of the population and does not influence that measure of the population.
Is a biased sample always valid?
a biased sample is valid determin
You want to do a survey on how religious people are in your town What do you need to have a valid scientific sample?
random sample of the town's population apex- (; A mix of participants that reflect your town's makeup
When the sample size is large valid confidence intervals can be established for the population mean irrespective of the shape of the underlying distribution?
Yes, but that begs the question: how large should the sample size be?
Why would someone want to assume that your sample data represents a population distrubution?
Most people take samples so that they may make estimates of parameters of interest: mean, variance, etc for the whole population. For such an estimate to have any validity the sample data must be assumed to represent a population distribution. Otherwise any conclusions based on the sample are valid only for the sample: hardly worth the effort!
What conditions are required to form a valid large-sample confidence interval for you?
1) What conditions are required to form a valid large-sample confidence interval for µ?
Why is it important to have a representative sample when doing surveys?
Because without representative sample, your results will not be valid.
What do you mean by sample design?
Sample design refers to the process of selecting a sample from a larger population for research or data analysis. The sample is a subset of the population, which is selected to represent the population's characteristics accurately. Sample design involves determining the size of the sample, the sampling method, and the criteria for inclusion in the sample. The size of the sample is typically determined based on the desired level of precision, level of confidence, and resources available for the research or data analysis. The sampling method can be random, stratified, cluster, or systematic, depending on the research question and the characteristics of the population. The criteria for inclusion in the sample are determined by the research question and the population's characteristics. For example, if the research question is about the prevalence of a particular disease in a population, the sample design may include criteria for age, gender, and other demographic variables to ensure that the sample represents the population's characteristics accurately. Sample design is a critical aspect of research and data analysis, as it directly affects the accuracy and generalizability of the results. A well-designed sample can help to minimize bias and increase the reliability of the results, while a poorly designed sample can lead to inaccurate or misleading conclusions. Therefore, it is essential to carefully consider sample design when conducting research or data analysis to ensure that the results are valid and reliable.
