First let me clear some things up:

I think you mean precision, not accuracy.

Accuracy is the abscence of bias, and this does not depend on the sample size but on the experimental details.

Precision can mean different things. The precision of a measurement depends on the experiment and the instrument (what is measured and and how it is measured) and refers to the differences between measurements of the same object/subject (-> technical). This kind of precision is typically quantified as the variance or SD.We could name this "between-measurements-variance", "within-object-variance" or "technical variance".

Measurements taken from different objects/subjects will also vary. The variance of these values will be the sum of the technical variance and the variance of the measured attribute among the objects/subjects. Let's call this the "between-objects-variance".

Based on such replicate mesurements of different objects/subjects one may want to estimate some parameter of a statistical model about the measured attribute. A very simple  example is the mean ("expected value"). Such an estimate has a precision that depends on the "between-objects-variance" and on the amount of available data (the sample size).

I hope you wil now understand that the information you provided is not sufficient to help you. It is not clear waht you mean with "result" and if you are really interested in accuracy or in precision.

In case you aim to find a sample size to get a particular precision of the estimated mean:

The SD of the mean is called the standard error (of the mean), SE. The value of the SE can be estimated from the SD of the data and the sample size N used to estimate the mean as SE = SD / sqrt(N). Now if you want to find the N to get a particular SE, all you need to do is to solve the equation for N:

N = (SD / SE)²

Example:

your SD is 532, and say you want the SE to be smaller than 100, then you will need to measure at least 29 values: N = (532 / 100)² = 28.3 (round up!)

You need to use the tool named "Power and size of the sample" in an statistical software as Minitab.

First let me clear some things up:

I think you mean precision, not accuracy.

Accuracy is the abscence of bias, and this does not depend on the sample size but on the experimental details.

Precision can mean different things. The precision of a measurement depends on the experiment and the instrument (what is measured and and how it is measured) and refers to the differences between measurements of the same object/subject (-> technical). This kind of precision is typically quantified as the variance or SD.We could name this "between-measurements-variance", "within-object-variance" or "technical variance".

Measurements taken from different objects/subjects will also vary. The variance of these values will be the sum of the technical variance and the variance of the measured attribute among the objects/subjects. Let's call this the "between-objects-variance".

Based on such replicate mesurements of different objects/subjects one may want to estimate some parameter of a statistical model about the measured attribute. A very simple  example is the mean ("expected value"). Such an estimate has a precision that depends on the "between-objects-variance" and on the amount of available data (the sample size).

I hope you wil now understand that the information you provided is not sufficient to help you. It is not clear waht you mean with "result" and if you are really interested in accuracy or in precision.

In case you aim to find a sample size to get a particular precision of the estimated mean:

The SD of the mean is called the standard error (of the mean), SE. The value of the SE can be estimated from the SD of the data and the sample size N used to estimate the mean as SE = SD / sqrt(N). Now if you want to find the N to get a particular SE, all you need to do is to solve the equation for N:

N = (SD / SE)²

Example:

your SD is 532, and say you want the SE to be smaller than 100, then you will need to measure at least 29 values: N = (532 / 100)² = 28.3 (round up!)

Thanks to both of you for taking time to reply my question. I think I meant precision in place of accuracy.

Dear Onome

The minimum sample size depends on the type of your population, is it finite population or infinite population.

In the case of finite population, you can use the attached table to estimate the suitable sample size. Or use Slovin formula :

n= N / ( 1+ N * e^2 ) 

But in case of infinite population: 

The sample size for any study depends on the standard deviation of the variable ( from previous studies ) and the margin of error you decided . The formula:

n= ( Z^2 * S^2) / E^2

where : Z  ( 1.96 for 0.05 and 2.58 for 0.01 )

S = standard deviation from previous studies or pilot  study

E = significant level 

Also, you can use software to calculate sample size ( SPSS , Minitab , G*power )

My advice to use G*Power .

G*Power software is effective tool to calculate sample size for many ranges of experiments. Also, you can determine effect size and power of the test, G*Power is free to download and easy to use after reading the manual, the download link:

http://download.cnet.com/G-Power/3000-2054_4-10647044.html

The link to download G*Power manual:

http://www.gpower.hhu.de/fileadmin/redaktion/Fakultaeten/Mathematisch-Naturwissenschaftliche_Fakultaet/Psychologie/AAP/gpower/GPowerManual.pdf

I hope that be useful for you.

Good Luck

Thank You Mr. Khalid, the information was helpful.