There is no "ideal" sample size for any given population, because polls and other statistical analysis forms depend on many factors, including what the survey is intended to show, who the target audience is, how much statistical error is permitted, and so on. The "Survey System" link, below, offers definitions and a couple of calculators to determine the best sample size for most purposes.
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I will assume the sample is random. In general, the larger the sample, the smaller the percentage error will be (the difference between percentages in the sample, and the percentages in the universe from whence the sample is taken). The percentage error tends to go down as the square root of the size of the sample.
I've included a couple of links. Statistical theory can never tell you how many samples you must take, all it can tell you the expected error that your sample should have given the variability of the data. Worked in reverse, you provide an expected error and the variability of the data, and statistical theory can tell you the corresponding sample size. The calculation methodology is given on the related links.
A sample size of one is sufficient to enable you to calculate a statistic.The sample size required for a "good" statistical estimate will depend on the variability of the characteristic being studied as well as the accuracy required in the result. A rare characteristic will require a large sample. A high degree of accuracy will also require a large sample.
+or- 5%
There is no "ideal" sample size for any given population, because polls and other statistical analysis forms depend on many factors, including what the survey is intended to show, who the target audience is, how much statistical error is permitted, and so on. The "Survey System" link, below, offers definitions and a couple of calculators to determine the best sample size for most purposes.
Statistical sampling is an objective approach using probability to make an inference about the population. The method will determine the sample size and the selection criteria of the sample. The reliability or confidence level of this type of sampling relates to the number of times per 100 the sample will represent the larger population. Non-statistical sampling relies on judgment to determine the sampling method,the sample size,and the selection items in the sample.
The "n" stands for the sample size within a statistical analysis. It represents the number of observations or data points used to calculate a statistic or estimate.
William C. Guenther has written: 'A sample size formula for the hypergeometric' -- subject(s): Hypergeometric distribution, Sampling (Statistics) 'Concepts of probability' -- subject(s): Probabilities 'A sample size formula for a non-central t test' -- subject(s): Sampling (Statistics), Statistical hypothesis testing, T-test (Statistics) 'Analysis of variance' -- subject(s): Analysis of variance
Factors that will affect the sample size calculation for a clinical trial include the effect size (magnitude of the treatment effect), desired level of confidence, statistical power, variability in outcome measures, and the type of statistical test being used. Additionally, the expected dropout rate, study design, and practical considerations such as cost and feasibility can also impact the sample size calculation.
Nancy Pei-ching Lin has written: 'A new approach to sample size determination of replicated Latin square designs and analysis of multiple comparison procedures' -- subject(s): Analysis of variance, Sampling (Statistics), Statistical hypothesis testing
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To compare the effect of two different fertilizers on plant growth, you would need identical plant species, same environmental conditions (light, water, temperature), controlled experimental setup, measured growth parameters, and a sufficient sample size for statistical analysis.
grain size analysis is used to accertain the various sizes that are available in a particular sample of soil since it is required to calculate the strenght of concrete mix and also the king of soil that the sample is.