Get Results You Can Trust
The right sample size ensures your survey results represent your target population accurately. A sample size that’s too small can lead to misleading conclusions, while an unnecessarily large sample wastes resources.
Our calculator helps you strike the perfect balance—ensuring confidence in your insights without overspending.
Key Benefits:
- Statistically reliable results
- Avoid wasted resources
- Tailored for any survey type
Three Simple Inputs. One Accurate Result.
Confidence Level
- This represents how certain you want to be about your results.
- A 95% confidence level means that if you conducted the survey 100 times, you’d expect the results to fall within the same range 95 times.
- Common options: 90% (moderate certainty), 95% (standard for most surveys), 99% (high certainty, but requires a larger sample size).
Margin of Error
- This indicates how much your results might differ from the actual population value.
- For example, a margin of error of 5% means your results could vary by ±5%.
- A smaller margin of error increases precision but requires a larger sample size.
Population Size
- The total number of people in the group you want to study.
- For large populations (over 100,000), the population size has minimal effect on sample size.
3. Population Proportion
- This is an estimate of how many people in your population might pick a specific response.
- If you’re unsure, using 50% (0.5) is a safe choice, as it represents the maximum variability.
- Example: If you’re asking, “Do you like chocolate?” and expect around 70% to say yes, your population proportion would be 70% (0.7).
Steps :
- Confidence Level:
- This represents how certain you want to be about your results.
- A 95% confidence level means that if you conducted the survey 100 times, you’d expect the results to fall within the same range 95 times.
- Common options: 90% (moderate certainty), 95% (standard for most surveys), 99% (high certainty, but requires a larger sample size).
- Margin of Error:
- This indicates how much your results might differ from the actual population value.
- For example, a margin of error of 5% means your results could vary by ±5%.
- A smaller margin of error increases precision but requires a larger sample size.
- Population Proportion:
- This is an estimate of how many people in your population might pick a specific response.
- If you’re unsure, using 50% (0.5) is a safe choice, as it represents the maximum variability.
- Example: If you’re asking, “Do you like chocolate?” and expect around 70% to say yes, your population proportion would be 70% (0.7).
- Population Size (Optional):
- The total number of people in the group you want to study.
- For large populations (over 100,000), the population size has minimal effect on sample size.
FAQ Section
99%, 95% and 90% are the most commonly used confidence levels in sample surveys. However, in most surveys used in marketing, employee feedback management, customer experience management and social sciences, 95% is the frequently used to balances accuracy with resource efficiency (cost and time). Usually, confidence levels below 90% are not recommended
A higher margin of error means less precise results. For critical decisions, aim for a smaller margin of error (e.g., 3-5%). For less critical insights, a margin of 7-10% might suffice. Think of margin of error like image resolution- higher the resolution, better you can differentiate between two points close to each other. Similarly, when making decisions, smaller the MoE better is your ability to choose between the choices
Yes! It works for customer feedback, employee engagement, market research, and more.
If you're unsure, use 50% (0.5). It represents the highest variability and ensures your sample size is sufficient.
Once a population reaches a certain size, increasing it further has little impact on the required sample size. This is why surveys of small towns and entire countries can sometimes require similar sample sizes.
You can increase sample size slightly or focus on a narrower target group to improve precision.
Yes. Quantitative surveys require statistically significant sample sizes, while qualitative surveys focus on in-depth responses from smaller groups. Do not aim for statistical significance in qualitative research
For small populations, use the finite population correction formula to adjust your sample size. Our calculator accounts for this automatically.
A smaller sample size can still provide reliable insights if it is representative of the population and meets statistical confidence and margin of error requirements.
A low response rate can affect data quality. Consider sending reminders, optimizing survey timing, or using incentives to boost participation.
Struggling to Achieve the Required Sample Size?
Merren delivers 10x higher response rates through it’s multichannel (Email, WhatsApp, chatbots) survey capabilities. Don’t let low sample size hold back your insights