How many market researchers does it take to change a light bulb? 30.
One to change the bulb, and 29 to get its significance. Oh, you think it’s a bad joke. I agree. But you and I make for a sample size of two and that is clearly not enough to make the conclusion.

A sample size calculation is perhaps one of the more complex decisions to make in a survey research study. It impacts how assured you would be in your ultimate findings. Plus a good sample size calculation can help you confidently pick from close choices. Surely, it also impacts how much you will end up spending.

So, how do you select an ideal size of the sample for your study if you are not an accomplished statistician. Here are the two major ways to calculate the right survey sample size:

What is a Homogenous Group?

If you are looking for a safe, can’t-go-wrong type thumb rule, pick an appropriate sample size of 400 (385 to be precise) per homogenous group. What is a homogenous group? It is a cluster of members whose behaviour is assumed to be similar to the other members of the group and different from non-members of the group. Typical examples of homogenous groups are males/ females, young/ old, smartphone users/non-user determine the target population.

So, is it that simple? Just take 400 of each number of respondents that form your customer base and you are ready to launch your study? Well, not really. Do that and your research is probably (almost certainly) going to go over-budget quickly.

The other end of this spectrum is the magic number of 30. You can choose a sample size of 30 (a little more, in case you provision for loss to data cleaning) for each of your homogenous group. Why 30 you ask? The easiest explanation is that if you pull 30 samples from a larger population randomly, these samples should start to mimic the population. Typically, that is what you want- Your sample should be a replica of the entire population you want to study. The more samples you pick, the better a replica it would be.

So why is this not the end of the discussion? Why not pick 30 per group every time? It makes two major assumptions- 

• The homogenous groups are truly homogeneous. 
• The randomization is truly random. Neither of these assumptions work well in real-life scenarios.

Can A Survey Sample Size Be Too Large?

Yes, a survey can have too large of a sample size. While a larger sample size can provide more precise results, it can also be costly and time-consuming. Strike a balance between statistical significance and practicality when determining the ideal survey sample size.

Borrow from the wisdom of the experts

• So, if not 30 or 400, then what? The next easiest route is to borrow from the wisdom of the experts and use some thumb rules. The table below gives some sample size thumb rules that are used by practitioners often.

Adapted from Hyman, Michael & Sierra, Jeremy. (2010). Selecting a sample size for your customer survey. NMSU Business Outlook.

But do remember – these are just thumb rules and should work in most cases you would encounter.But they will not work in all scenarios so handle with care.

What is the Right Survey Sample Size?

A small sample size may not get you to the right conclusion of your study. However, a larger sample size might sound ideal. However, it might be prone to error or mismanagement of results. Either way, you might not get the desired results. 

As per some statisticians, a sample size of minimum 100 participants can give you meaningful results. However, in certain comparison studies, researchers might look for a minimum of 5 participants per group. This is applicable in the field of study such as pharmacology and biological research. For clinical studies, researchers prefer having at least 100 participants in each group. 

It is important to note that the minimum and maximum sampling depends on the time and budget of the research. It is stated that a good maximum sample size can be 10% of the chosen population. It should not exceed more than 1000 participants. Taking more than 10% will bring more or less the same results. 

In a population of 6000, 10% of this number will be 600. However, in larger cases, the number will be greater than 1000. 

For example, 10% of 30,000 people will be 3000 which is more than 1000. So you can take the maximum number i.e 1000 people. 

It is recommended that one can choose the minimum respondents if the conclusion of the study will not have much consequences. Researchers must choose the maximum if the conclusions of the study can cause a serious impact on the rest of the population.

How to calculate the sample size for your survey?

1. Use online survey sample size calculator:

Use an online sample size formula with calculators like the one here. It will require you to understand concepts like margin of error, confidence level and response distribution, but the site does a good job of explaining the basics.

2. Calculate the sample size with this formula:

If you are feeling adventurous and want to calculate the sample size yourself, why not? If your target group of people has a large size of the population, you can use the following formula:

In this formula, n is the sample size

z is the confidence interval for the desired level of confidence (you can find z tables here)

s = estimated standard deviation

e = desired margin of error

At most times, the right sample size selection can be a trade-off between expected precision and cost to attain it. Getting the perfect sample size is critical to confident decision making.

3. Apply the rule of thumb: 385 per group

For quick estimates, researchers often use a rule of thumb: 385 responses per homogeneous group delivers a 5% margin of error and 95% confidence level.

Example

• Surveying a company with 5,000 employees? Use 385.
• Two distinct groups (e.g., managers and staff)? Aim for 385 each, totaling 770.

This number comes from statistical tables and aligns with Slovin’s Formula for large populations. It’s a fast, no-math option.

Survey Sample Size Calculator –Use Slovin’s Formula Calculator for Precision

In addition to the methods previously discussed, another useful approach is Slovin’s Formula Calculator. This formula is particularly helpful when you have limited information about your population’s behavior or variability.

What is Slovin’s Formula? 

Slovin’s Formula calculates the sample size needed from a known population with a specific margin of error. It’s ideal for surveys where you want statistically valid results without surveying everyone.

Slovin’s formula is calculated as follows:

n = N / (1+Ne^2)

In this formula:

• n = Sample size
• N = Total population size
• e = Desired margin of error, expressed as a decimal (e.g., 0.05 for 5%)

Calculate Slovin’s formula with an example

Let’s walk through an example to see how Slovin’s formula can be applied:

1. Define the population size (N): Suppose you’re surveying a city with 50,000 residents. Here, N= 50,000.
   
   
2. Determine the margin of error (e): Decide on an acceptable margin of error. For instance, if you choose a 5% margin of error, then e=0.05.
   
   
3. Calculate the sample size (n):
   n= 50,000 / 1+50,000×0.05^2
   n= 50,000 / 1+50,000×0.0025
   n= 50,000 / 1 + 125
   n= 50,000 / 126
   n ≈397.6 

So you would need a sample size of approximately 398 respondents to achieve a 5% margin of error in your survey. 

When to Use Slovin’s Formula?

• Best for large, known populations (e.g., employees, customers).
• Assumes a 95% confidence level (standard in research).
• Works well with a homogenous group.

Limitations of Slovin’s formula

While Slovin’s Formula is straightforward and useful in many scenarios, it’s important to note its limitations:

• Assumption of Random Sampling: The formula assumes that your sample is randomly selected from the population. Any bias in sampling can affect the accuracy of your results.
• Lack of Confidence Level Specification: Unlike other sample size calculation methods, Slovin’s Formula does not explicitly account for confidence levels. Confidence levels are crucial in determining the reliability of your survey results.
• Applicability to Large Populations: The formula is most effective for large populations. In cases of small populations, other sampling methods might be more appropriate.

Comparing Slovin’s Formula to Other Methods

Frequently Asked Questions: 

What’s a good margin of error?

A 5% margin (e.g., ±5%) is typical for most surveys, balancing accuracy and effort.

Can I use Slovin’s Formula for small groups?

It’s less reliable below 200—consult an expert or use a calculator instead.

How do I try a Slovin’s Formula Calculator?

Use tools from Merren to calculate the right survey sample size or check out free online options.

Conclusion

Merren offers a  free 14 day trial without any credit card commitments. Increase your response rate with the right sample size using Merren’s responsive surveys. Get access to AI-driven survey analysis on your dashboard.