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How many people need to take your survey? Our sample size calculator makes collecting the correct number of responses easy.
Sample size is the number of completed responses your survey receives. It should represent the target population whose opinions or behavior you care about.
Let’s explore:
Here are key terms you’ll need to understand to calculate your sample size:
Population size represents the total number of people in the group you are trying to study. If you were surveying people in the United States, the population size would be around 333 million. When surveying your company, your population size will be the total number of employees.
The margin of error is a percentage that shows how accurately survey results reflect the opinions of the whole population. The lower the margin of error, the more accurate the answer is at a given confidence level.
Confidence level measures how sure you can be that the population will choose an answer within a certain range. For example, a 95% confidence level means you can be 95% certain the results lie between x and y numbers.
Confidence interval: The confidence interval represents a statistical range that indicates where the true result is probably. For example, a 95% confidence interval indicates that if you sampled the same population numerous times, your true result would lie within the interval in approximately 95% of the samples.
How do you determine the minimum sample size for your survey? If you’d like to do the calculation by hand, use the following formula:
N = population size • e = Margin of error (percentage in decimal form) • z = z-score
The z-score is the number of standard deviations a given proportion is away from the mean. To find the right z-score to use, refer to the table below:
Desired confidence level | z-score |
80% | 1.28 |
85% | 1.44 |
90% | 1.65 |
95% | 1.96 |
99% | 2.58 |
Let’s work through some sample size examples:
Suppose you’d like to survey delivery workers in a city with a population of 500,000. You want your sample size to have a 95% confidence level and a margin of error of 5%. Using the formula (or calculator above), you can calculate your sample size to 384.
In contrast, if you conducted a company-wide survey with 300 employees and wanted an 80% confidence level and a 10% margin of error, your sample size would be 37.
Your survey sample size will often depend on your study’s objective and the population you’re surveying.
For example, you may only need to include some customers when using a customer feedback survey. On the other hand, political pollsters must carefully select a balanced sample size to reflect the overall population accurately.
Here’s how each value can affect your survey:
The effect survey values have on the accuracy of its results
Value increased | Value decreased | |
Population size | Accuracy decreases | Accuracy increases |
Sample size | Accuracy increases | Accuracy decreases |
Confidence level | Accuracy increases | Accuracy decreases |
Margin of error | Accuracy decreases | Accuracy increases |
Specific use cases can help determine whether to use a statistically significant sample size:
There isn’t a universal standard for determining a large sample size. Your sample size will often vary depending on the context, such as the field of study or the research goal.
A large sample size typically provides enough statistical power to detect meaningful differences in your studied population. In many fields, experts consider a sample size of several hundred or more to be large.
However, researchers decide how many samples to use based on factors like statistical power or available resources. Your sample size may also depend on the type of analysis you’re conducting. Some statistical tests may require larger sample sizes than others.
Additionally, it's worth considering that the population's complexity can determine how large your sample size is. A larger sample size may be necessary for highly diverse populations or when studying rare phenomena.
Use these tips to calculate the best sample size for your survey:
The purpose of your data can determine your sample size. If your sample size is too small, the survey’s results may not be accurate. If it's too large, the survey can be expensive or require more time to complete.
When collecting survey data, you may need a bigger sample size to get accurate results or apply findings to a larger group. Smaller samples could be enough if you use the sample survey data primarily for qualitative insights.
Larger samples can be more costly due to data collection expenses, participant incentives, and analysis. With a smaller budget, you may opt for a smaller sample size. Time constraints may also influence sample sizes.
If you don't have much time for the survey, use a smaller sample size to gather accurate data quickly. If time allows, aim for a larger sample size to increase the precision of your results.
Different survey types may require different approaches to sample size determination. Customer feedback surveys are helpful with smaller sample sizes. Political polls, on the other hand, require larger and more representative samples. Tailor your approach to ensure that the results are statistically sound and reliable.
Open-ended questions ask for detailed answers, making surveys take longer to finish because respondents need to write more. As a result, some respondents may not finish your survey, making it harder to achieve a large sample size. Adjust the number of open-ended questions or plan your sample size accordingly.
Failing to address common sample size mistakes can undermine your survey's effectiveness. When calculating your survey’s sample size, avoid these potential pitfalls:
Use our sample size calculator for better survey results. If you need more respondents, use SurveyMonkey Audience to find survey participants globally and collect responses from almost anyone.
Insights managers can use this toolkit to help you deliver compelling, actionable insights to support stakeholders and reach the right audiences.