Sample Size Calculator FAQs
What is the Minimum Survey Sample Size Needed to Collect Valid Data?
The answer depends on three factors: population size, confidence level, and the acceptable margin of error. Use the sample calculator above to determine how many subjects you need to sample based on your population size.
How Does Choosing the Right Sample Size Improve the Quality of Survey Data?
With the right sample size, you can collect responses that accurately represent the entire population of your survey. Calculating the right sample size reduces errors, increases reliability, and provides clear, accurate insights you can use to make data-backed decisions.
Does Sample Size Determine Whether or Not My Survey Provides Statistically Significant Results?
Yes. A larger sample size reduces the margin of error and increases the reliability of your results, which makes it more likely that differences or patterns are statistically significant and not due to random chance.
But there are limits.
While it’s true that a larger sample size reduces the margin of error, it also requires a larger investment of time, energy, and budget to collect data from everyone in your sample. Choosing an appropriate sample size is the best way to collect valid data without overextending your resources.
What is the Formula Used to Manually Calculate Sample Size?
The image below shows the sample size formula:
Where:
- n = Sample size (the number of responses needed)
- Z = Z-score, corresponding to your desired confidence level (e.g., 1.96 for 95%)
- p = Estimated population proportion (also called the sample proportion; use 0.5 if unknown)
- e = Margin of error, expressed as a decimal (e.g., 0.05 for 5%)
This formula gives you the minimum sample size required to ensure your sample estimate is accurate and representative of the population. However, for CX teams, performing a manual sample size estimation can be time-consuming and prone to error. To save time and headaches, we recommend using our sample size calculator instead.
What is “Statistical Power?”
Statistical power is the likelihood that a test will reveal a difference if one truly exists. It measures the ability of a survey or experiment to avoid a Type II error, which occurs when you fail to identify a real effect.
To put it simply, with enough statistical power, your test is more likely to identify meaningful differences or relationships within your data.
Should I Sample Subgroups of My Target Population Separately?
Yes, you should sample subgroups of the same population separately to ensure proportional representation. This helps your survey reflect the diversity of the overall population, which makes the results more accurate.
For instance, if certain sub-populations (like age groups) make up different proportions of your audience, sampling them proportionately helps you capture their unique perspectives fairly without over- or under-representing one subgroup over another.
What Terms Should I Understand Before I Use the Sample Size Calculator?
Below is a brief glossary of terms related to the sample size calculator. It’s not exhaustive, but it covers many key terms related to statistics.
Random Sample: A random sample ensures that every individual in your target population has an equal chance of being selected. This reduces bias and improves the reliability of your results.
Margin of Error: The lower your margin of error, the more accurate your results will be. A 5% margin of error is often used in polling studies, and it’s generally the norm. Just keep in mind that a 5% margin of error is a plus or minus figure. That means your margin of error is actually a 10% spread, because you could be plus OR minus 5%.
Confidence Level: Statisticians often refer to a confidence level, and here is a simplistic explanation. Let’s assume you set your confidence level to 95%. If you were to repeat your experiment over and over again, the results would match the results from the actual population 95% of the time, or 19 times out of 20.
When you combine your margin of error (for example, 5%) and your confidence level (for example, 95%), you can say that you are 95% sure that the true measurement of the general population is within 5% of your survey results.
Confidence Interval: This is the range within which the true value of your survey results is likely to fall. For example, if your survey shows 60% satisfaction with a confidence interval of ±5%, the true satisfaction rate is likely between 55% and 65%.
Confidence Level Z-Score: The Z-score corresponds to your chosen confidence level. For example, a 95% confidence level has a Z-score of 1.96. It’s a statistical value that helps calculate the margin of error and sample size.
Standard Deviation: This measures the variability or spread of responses in your data. A larger standard deviation means responses vary widely, while a smaller one indicates more consistency.