Our survey sample size calculator can help determine how many people are needed to carry out a successful survey that is statistically significant.
When considering conducting a survey, one of the first questions is how many responses are needed for a statistically significant dataset. Determining the right survey sample size depends on many factors. But with our sample size calculator, you can easily find the ideal sample size for a survey—online and for free. Using it is easy, though understanding the basic principles of getting data that actually matters is the first step.
The number of people your sample will represent.
The industry standard is 95%.
Typically between 4% and 8%.
Getting an individual survey response from everyone in any sizable group is impossible. What can be done is to receive responses from a relevant portion of the group. This will provide comprehensive data from which to draw conclusions that can help achieve goals.
This is where the questionnaire sample size comes into play. Using this calculation, you know if you’ve gathered enough responses from a certain subgroup to be statistically significant. In other words, you want a representative sample that’s large enough to produce statistically significant results.
It’s required to know:
Before diving deeper into the math behind the survey sample size formula, let’s explain some basic terms:
When calculating sample size for survey research, it’s important to know a few facts. The first is the population of the group being researched. If you’re conducting patient surveys for your healthcare institution, it’s important to know how many patients there are. Every patient does not need to fill out the survey, though if there are 500 patients then it’s unlikely that only 10 responses will produce a statistically significant result.
A good sample size for an online survey can vary depending on several factors, including the population size, the level of precision desired, and the level of confidence desired. However, a sample size of at least 500-1000 respondents is recommended for online surveys.
This is because online surveys tend to have lower response rates than other surveys. A larger sample size can help compensate for this. Additionally, online surveys may have a higher degree of non-response bias, which can be mitigated by increasing the sample size.
When conducting an online survey, a sample size calculation should consider the potential non-response bias—meaning non-respondents may have different characteristics from respondents. The sample size should be adjusted accordingly.
Learn how to avoid biased survey questions by reading our blog about it.
Don’t let the statistical terminology confuse you. The margin of error is a percentage—from 0 to 100 percent—which tells you how much the results from the sample will vary compared to the entire population.
The margin of error should be as small as possible. This is achieved by having a large enough sample, since the margin of error is inversely proportional to sample size. The bigger the sample size, the smaller the margin of error.
The survey sample size confidence interval provides the level of uncertainty in your results. Every statistic (for example, 56 percent of the US population watches TV after work) is not 100 percent certain. The confidence interval tells you how uncertain a particular statistic is.
Along with the confidence interval, there is also the confidence level, which can be 99 percent, 95 percent, 90 percent, etc. Confidence level (for example, 95 percent) tells you that, if the survey is repeated, the results will be in a certain range 95 percent of the time.
If you want a survey with a high confidence level—in the 99 to 95 percent range—a large sample is needed. Luckily, our sample size calculator returns exactly how many people will need to complete your survey based on chosen parameters.
It’s important to note that having statistically significant responses is not always important. If you have a retail company and you’re conducting customer surveys, then every response matters. For example, you won’t ignore a certain problem that some customers have with a product, even if only a few bring it up in a survey. In practice, every response holds immense value.
On the other hand, when it comes to public sector surveys, having a large sample and statistically significant data is important. If the survey is about transportation priorities in Washington, DC, then it’s of utmost importance to gather responses from as many citizens as possible in order to best develop priorities. This will ensure that the majority is satisfied with the decisions that impact their everyday life.
Now that you know the basic terminology and math behind the sample size calculator, it’s time for an actual sample size formula.
For example, if you’re surveying a population that has 1000 people—and you want your confidence level to be 95 percent (with a 5 percent margin of error)—then the minimum survey sample size for statistical significance would be 278 responses.
A good sample size for a survey depends on several factors, including the population size, the level of precision desired, and the level of confidence desired. A general rule of thumb is that a sample size of at least 30 is needed for accurate results. But for larger populations, a larger sample size may be necessary to achieve the desired level of precision and confidence.
Figuring the sample size for a survey is hassle-free with our statistically significant sample size calculator. It will tell you precisely how many responses are needed without having to make calculations manually. Try it out before focusing on which survey is the best fit.
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