If You Want To Be 99% Confident, Then How Large Should Your Sample Size Be?

Sample Size Calculator

This Sample Size Computer is presented as a public service of Creative Research Systems survey software. You can use it to decide how many people you need to interview in order to become results that reflect the target population as precisely every bit needed. You tin can also find the level of precision you have in an existing sample.

Before using the sample size calculator, at that place are 2 terms that you need to know. These are: confidence interval and confidence level. If you are not familiar with these terms, click hither. To learn more about the factors that affect the size of confidence intervals, click here.

Enter your choices in a estimator below to find the sample size yous need or the confidence interval you lot have. Leave the Population box blank, if the population is very large or unknown.

Sample Size Calculator Terms: Confidence Interval & Conviction Level

The confidence interval (also called margin of error) is the plus-or-minus figure normally reported in paper or television opinion poll results. For example, if you use a confidence interval of iv and 47% percent of your sample picks an reply you can be "certain" that if y'all had asked the question of the entire relevant population between 43% (47-4) and 51% (47+iv) would have picked that answer.

The confidence level tells you how sure you can exist. It is expressed as a percentage and represents how frequently the true per centum of the population who would pick an answer lies within the confidence interval. The 95% conviction level means you tin be 95% sure; the 99% conviction level means you can exist 99% sure. About researchers use the 95% confidence level.

When you put the confidence level and the conviction interval together, you can say that you are 95% sure that the true per centum of the population is between 43% and 51%. The wider the confidence interval you are willing to accept, the more certain yous can be that the whole population answers would be inside that range.

For example, if yous asked a sample of 1000 people in a city which brand of cola they preferred, and 60% said Brand A, you can be very certain that between 40 and eighty% of all the people in the city actually do prefer that brand, simply you cannot be and then sure that between 59 and 61% of the people in the city prefer the brand.

Factors that Bear upon Confidence Intervals

There are 3 factors that determine the size of the confidence interval for a given confidence level:

Sample size
Percentage
Population size

Sample Size

The larger your sample size, the more sure you tin can be that their answers truly reverberate the population. This indicates that for a given confidence level, the larger your sample size, the smaller your confidence interval. Nevertheless, the relationship is not linear (i.eastward., doubling the sample size does not halve the confidence interval).

Percent

Your accurateness also depends on the pct of your sample that picks a particular answer. If 99% of your sample said "Yep" and i% said "No," the chances of error are remote, irrespective of sample size. However, if the percentages are 51% and 49% the chances of error are much greater. It is easier to be certain of extreme answers than of middle-of-the-road ones.

When determining the sample size needed for a given level of accuracy you must utilise the worst case pct (50%). Yous should likewise use this percentage if you lot want to determine a general level of accuracy for a sample you already have. To determine the conviction interval for a specific answer your sample has given, y'all tin apply the percentage picking that answer and get a smaller interval.

Population Size

How many people are there in the group your sample represents? This may be the number of people in a city yous are studying, the number of people who buy new cars, etc. Often you may not know the exact population size. This is not a problem. The mathematics of probability prove that the size of the population is irrelevant unless the size of the sample exceeds a few percentage of the total population you are examining. This ways that a sample of 500 people is equally useful in examining the opinions of a state of 15,000,000 as it would a city of 100,000. For this reason, The Survey System ignores the population size when it is "big" or unknown. Population size is only probable to be a factor when y'all work with a relatively small and known group of people (e.1000., the members of an association).

The confidence interval calculations assume you lot accept a genuine random sample of the relevant population. If your sample is not truly random, you lot cannot rely on the intervals. Non-random samples usually result from some flaw or limitation in the sampling process. An example of such a flaw is to just call people during the twenty-four hour period and miss almost everyone who works. For most purposes, the non-working population cannot be assumed to accurately represent the entire (working and non-working) population. An instance of a limitation is using an opt-in online poll, such as one promoted on a website. There is no way to be sure an opt-in poll truly represents the population of interest.

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