When conducting a survey, the right sample size will provide statistically significant results. A well-calculated sample size saves time, reduces costs, and increases confidence in your findings. One popular method is Slovin’s Formula, a simple yet effective tool for researchers. In this blog, we will explore 4 proven methods to select a good survey sample size. We will also explain the significance of Slovin’s Formula. Use our free Sample Size Calculator to simplify the process!

What is a Homogenous Group (in A Survey Sample Size)?

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. A homogeneous group 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.

Why does sample size matter in a survey? 

A sample size that’s too small may lead to inaccurate conclusions. An overly large sample can waste resources. Factors like population size, margin of error, and confidence level influence the ideal sample size. Whether you’re conducting market research or customer feedback surveys, understanding how to calculate sample size is essential. Slovin’s Formula is particularly useful for large populations when you need a quick, reliable estimate.

4 Ways To Calculate The Sample Size For Your Survey

1. Use online survey sample size calculator:

Merren is an AI-powered customer research platform that also offers user-friendly sample size calculators that account for confidence levels, margins of error and population sizes. These tools often go beyond Slovin’s Formula by incorporating confidence intervals and variability estimates.

How to Use:  

• Input your population size (e.g., 100,000).  
• Select a confidence level (e.g., 95%, corresponding to a z-score of 1.96).  
• Choose a margin of error (e.g., 5%).  
• Get an instant sample size recommendation.

Merren’s Advantage: Our calculator supports advanced features like stratified sampling and multilingual surveys, ensuring accurate results for diverse audiences. Sign up for a 14-day free trial to try it now! 

2. Calculate the survey 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. Use statistical power analysis

For advanced research (clinical trials or comparative studies) statistical power analysis ensures your sample size is sufficient to detect meaningful effects. This method considers:

Effect size: The magnitude of the difference you expect to find.

Power: The probability of detecting an effect (typically 80% or 0.8).

Significance level: The threshold for statistical significance (e.g., 0.05 for 95% confidence).

Example:

In a study comparing two marketing campaigns, you might need a sample size of 200 per group to detect a 15% difference in conversion rates with 80% power.

4. Apply the rule of thumb for homogenous group

For quick estimates, a sample size of around 400 (precisely 385) per homogeneous group is a safe bet for most surveys. This rule assumes a 95% confidence level and a 5% margin of error, making it a reliable starting point for general market research or customer feedback surveys.

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.

When to use? 

• When you lack detailed population data.
• For exploratory surveys or when time is limited.

Limitation is that it may not suffice for highly diverse populations. It can oversimplify complex studies that need greater precision, especially studies that need intense research. 

Survey Sample Size Calculator: Use Slovin’s Formula Calculator for Data Collection

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.

Key terms in Slovin’s formula

To use Slovin’s Formula effectively, understand these core concepts:

• Population size (N): The total number of individuals in the group you’re studying. For example, if surveying a company’s 10,000 employees, N = 10,000. Accurate N is crucial; overestimate, and your sample might be too small.
• Margin of error (e): This represents how much your sample results might differ from the true population. A smaller e (e.g., 0.03 for ±3%) requires a larger sample but increases precision. Common values: 0.05 (95% of surveys), 0.03 (high-stakes research), or 0.10 (exploratory studies).
• Confidence level (implied): Slovin’s assumes 95% confidence, meaning if you repeated the survey 100 times, results would fall within the margin of error 95 times. For other levels (e.g., 99%), adjust with z-scores in modified formulas.
• Standard deviation/variability (not directly included): Unlike some formulas, Slovin’s doesn’t account for this explicitly, assuming maximum variability (p=0.5 for proportions). This makes it conservative but less tailored.

What is Slovin’s Formula? 

Slovin’s Formula calculates the sample size needed from a known population with a specific margin of error. It is useful when you have limited data on population variability, making it a go-to for quick, practical estimates in survey research. Unlike more complex formulas, Slovin’s assumes a 95% confidence level and focuses on achieving a desired margin of error.

Slovin’s Formula is ideal for scenarios like customer feedback surveys, employee opinion polls or market research where the population is finite and known (e.g: all users of your app or residents in a district). However, it’s been criticized for its simplistic assumptions and lack of formal statistical derivation. Some experts prefer alternatives like Cochran’s Formula for greater precision. Despite this, it remains popular for its ease of use.

Slovin’s formula is calculated as follows:

n = N / (1+Ne^2)

In this formula:

• n = The required sample size (round up to the nearest whole number).
• N = Total population size
• e = The margin of error, expressed as a decimal (e.g: 0.05 for ±5%)

This calculation ensures your survey results are representative without over-sampling.

Slovin’s Formula Examples: Step-by-Step Calculations

Let’s apply Slovin’s Formula with real-world examples for both proportions and means to illustrate its perfect use: 

Example 1: Estimating a proportion (survey on opinions)

Suppose you’re conducting a city-wide survey on resident satisfaction with public transport, with a population of 50,000 (N=50,000). You want a 5% margin of error (e=0.05).

1. Plug in the values: n = 50,000 / (1 + 50,000 × 0.05²)
   
2. Calculate: 0.05² = 0.0025
   50,000 × 0.0025 = 125
   1 + 125 = 126
   n = 50,000 / 126 ≈ 396.83
   
3. Round up: n = 397

With 397 responses, you can be 95% confident your results reflect the population within ±5%.

Example 2: Estimating a mean (e.g., average score)

Imagine polling 1,000 students (N=1,000) on their average study hours per week, aiming for a 3% margin of error (e=0.03). Slovin’s works here too, though it’s more common for proportions.

1. Plug in: n = 1,000 / (1 + 1,000 × 0.03²)
2. Calculate: 0.03² = 0.0009
   1,000 × 0.0009 = 0.9
   1 + 0.9 = 1.9
   n = 1,000 / 1.9 ≈ 526.32
3. Round up: n = 527

When to Use Slovin’s Formula in Surveys?

1. Large, known populations: Perfect for employee bases, customer lists, or community groups where N is clear.
2. Homogeneous groups: Assumes uniform variability; best when the population isn’t highly diverse.
3. Quick estimates: Use for pilot studies or when detailed data (e.g., standard deviation) is unavailable.
4. Real-world applications: Market research (e.g., product preference), academic surveys (e.g., student feedback), or public opinion polls.

Tips for successful application:

Avoid it for very small populations (N<100), where full censuses might be better, or when you need to specify custom confidence levels.

• Ensure random sampling to avoid bias.
• Verify your population estimate is accurate.
• Consider adjusting the margin of error based on your study’s precision needs.

Limitations and criticisms of Slovin’s formula

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

• Assumes 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.
• Applicable to large populations: The formula is most effective for large populations. For small groups, it may overestimate sample needs. Choose other sampling methods.

Slovin’s Formula vs Other Sample Size Collection 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.