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There is no simple answer. There are two main factors need to be taken into account. Consider the simple case of a dichotomous or binary variable.

One consideration is the consequences of getting the proportion wrong. If you are estimating the proportion of males (and females) going to a cinema so as to design the correct number of toilets, a 5% risk of getting it wrong may be acceptable. You may have some disgruntled customers and, in any case, it may be possible to rebuild and re-designate some toilets. If, instead, you are estimating the proportion of people who have a serious adverse reaction to some medication, a 5% error rate is catastrophic! Not just for the patient but for the pharmaceutical company as well.

Such risk assessment will determine the confidence level that you require from the estimate. Suppose now that for the study under consideration, a 5% risk of getting it wrong is acceptable. That is, you want to be 95% confident that the true (but unknown proportion) is within 1.96 standard errors of your estimate.

If the true proportion is around 50%, then a sample size of just under 100 will suffice. However, if you are trying to estimate the proportion of a rare characteristic - whose true incidence in the population is 0.5% - then for the same degree of confidence in the estimate you will need a sample of over 19,000.

Q: How do you determine the sample size to estimate the proportion?

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The answer depends on how rare or common the selected trait is. For something that is very rare, you will need a much larger sample to get a reasonable estimate of proportion.

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You can estimate a population's size when counting individuals if the density in a sample is greater than the population density.

According to the website Survey System's Creative Research Systems page, you can use a sample size calculator to determine how many people need to be interviewed in order to meet your target.

It is the number of elements in the sample. By contrast, the relative sample size is the absolute sample size divided by the population size.

Related questions

The answer depends on how rare or common the selected trait is. For something that is very rare, you will need a much larger sample to get a reasonable estimate of proportion.

A sample size is needed whenever you conduct an experiment. How you determine an adequate sample size depends on the scope of what you're testing, such as medications.

Factors that determine sample size

True.

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You can estimate a population's size when counting individuals if the density in a sample is greater than the population density.

They do not. Population size does not affect the sample size. The variability of the characteristic that you are trying to measure and the required accuracy will determine the appropriate sample size.

The mean of a proportion, p, is X/n; where X is the number of instances & n is the sample size; and its standard deviation is sqrt[p(1-p)]

1. population to deal with in the sample 2. Location. ocation where the sample will be done 3. design. how the sample will be taken 4. result. how the outcome will be determined

the larger you r sample size the better your estimate. imagine take the height of person to estimate the average high of an adult male. would one person's height be a good estimate, or would taking the average height of 100, or 5000 adult males will produce a better estimate?

Standard error of the sample mean is calculated dividing the the sample estimate of population standard deviation ("sample standard deviation") by the square root of sample size.

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.