Your question is a bit ambiguous, but I will provide the equation commonly used to calculate the necessary sample size in estimating the proportion of a distribution:
n = (z @ half-alpha/(2*Error))^2
z is commonly called the "Z score" and the error is given in terms of +/-. I refer this as z @ half alpha, so if my confidence level is 0.99, alpha is 0.01 and Z is evaluated at p= 1-0.1/2 of 0.995. The z value is found from tables of the normal distribution.
Example: I want to take a survey if people like Republicans or Democrats, I want to a 99% confidence limit that my survey proportion is correct within +/- 0.06.
z for .995 is 2.576, so n = (2.578/.12)^2 = 461 samples.
Note that this is the minimum size given the parameters established for my survey. If the survey question is biased, or if the selection of respondants is non-random, the above relationship beween error and sample size does not hold.
Sampling distributions of statistics are at the heart of both confidence intervals and sample size. Generally, the underlying concept is that the errors that make up the uncertainty in estimates are small and independent (random), so as the sample size increases, the accuracy of results will increase. This is not always true in nature, as in the case where more data leads to a higher percentage of flawed data.
I hope this answers your question. If not, please rephrase. Also, you may find a lot more information under "sample size" and "statistics" by searching wikipedia and other internet sites.
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statistics
Formula for standard error (SEM) is standard deviation divided by the square root of the sample size, or s/sqrt(n). SEM = 100/sqrt25 = 100/5 = 20.
Factors that determine sample size
Usually the sum of squared deviations from the mean is divided by n-1, where n is the number of observations in the sample.
When something is a sample size, that means it is smaller than the size that is normally available for purchase. Sample size products are usually enough to let you try something before you buy it.
The formula for calculating uncertainty in a dataset using the standard deviation is to divide the standard deviation by the square root of the sample size.
The formula for calculating the mean of a sample, represented by the symbol "" in statistics, is to add up all the values in the sample and then divide by the total number of values in the sample. This can be written as: x / n, where x represents the sum of all values in the sample and n is the total number of values in the sample.
The formula for calculating the spot size of a laser beam is given by: Spot Size 2.44 (wavelength focal length) / beam diameter
statistics
Statistics
width by height in centimeters
yes
Slovin's formula is a mathematical formula used to determine the sample size needed for a survey or study. It takes into account the population size, desired level of confidence, and margin of error to calculate the appropriate sample size for a given study. It is commonly used in statistics and research to ensure accurate and reliable results.
The same basic formula is used to calculate the sample or population mean. The sample mean is x bar and the population mean is mu. Add all the values in the sample or population and divide by the number of data values.
by using the capture-recapture method which gives you the total size of organisms in a population.
For the size in gallons for a rectangular aquarium, the formula is: (Length x Width x Height) divided by 231
The bulk density of magnetite can be calculated using the formula: Bulk Density = (mass of material) / (volume of material). This formula involves measuring the mass of the magnetite sample and calculating its volume to determine the bulk density.