Sample Size Calculator for Reliable Survey Results (2024)

Are you wondering why calculating sample size is important?

With the right sample size, a company can make accurate decisions, save time and money, reduce errors, and improve confidence in the results. This efficiency and reliability help the company obtain valuable insights without unnecessary effort or cost.

Sample size refers to the number of individuals or observations in a survey or study.

It is a subset of the larger population that researchers are interested in understanding. The purpose of selecting a sample size is to gather data that accurately reflects the population's characteristics, opinions, or behaviors without surveying everyone.

Imagine a company that wants to understand customer satisfaction with a new product. The product has been sold to 10,000 customers, but surveying all 10,000 customers would take time and effort. Instead, the company surveys a smaller group, or sample, of customers. 

To determine an appropriate sample size, they consider several factors:

Confidence Level: The company wants to be 95% confident in the survey results.
Margin of Error: They are willing to accept a margin of error of 5%.
Population Variability: They estimate that customer opinions might vary significantly.

Using a sample size calculation formula, they determined that surveying 370 customers out of the 10,000 would provide reliable and representative results. This sample size ensures that the findings accurately reflect the overall customer satisfaction without surveying everyone.

To calculate the sample size for a survey or study, you need to consider several key metrics:

The total number of individuals in the group you want to study. A general estimate can be used if the population is large or infinite.

The range within which you expect the true population parameter to fall. A smaller margin of error requires a larger sample size.

The probability that the margin of error contains the true population parameter. Common confidence levels are 90%, 95%, and 99%. Higher confidence levels require larger sample sizes.

The degree to which the population's data points are spread out. If this is unknown, a standard deviation of 0.5 is often used as a conservative estimate.

The estimated proportion of the population that has the attribute of interest. If unknown, 0.5 is used as it maximizes the required sample size.

Calculating sample size involves using specific formulas considering the desired confidence level, margin of error, population size, and population variability.

Here's a step-by-step guide to calculate sample size for a proportion:

The total number of individuals in the group you want to study. A general estimate can be used if the population is large or infinite.

Confidence Level (Z): Common values are 1.96 for 95% confidence, 1.645 for 90% confidence, and 2.576 for 99% confidence.
The margin of Error (E): This is the desired precision level, such as ±5% (0.05).
Estimated Proportion (P): Using 0.5 as a conservative estimate if unknown.
Population Size (N): The total number of people.

The range within which you expect the true population parameter to fall. A smaller margin of error requires a larger sample size.

If the population size (N) is known and finite, adjust the sample size using:

Smaller Margin of Error: Achieving a smaller margin of error requires a larger sample size for the same population.
Higher Confidence Level: A higher confidence level necessitates a larger sample size.

Here’s how to categorize sample sizes based on different types of surveys:

Purpose: To understand consumer behavior, preferences, or product feedback.
Typical Sample Size: 100 to 1,000 participants.
Example: For a new product launch, a sample size of 400 might be used to gauge customer interest and feedback.

Purpose: To measure customer satisfaction and identify areas for improvement.
Typical Sample Size: 200 to 1,000 participants.
Example: A retail store might survey 500 customers to get a representative view of their satisfaction with recent service changes.

Purpose: To assess employee satisfaction, engagement, and workplace culture.
Typical Sample Size: 30 to 300 employees, depending on the total number of employees.
Example: For a company with 1,000 employees, surveying 250 employees might provide a good understanding of overall engagement levels.

Purpose: To gather data on patient health, treatment effectiveness, or satisfaction with healthcare services.
Typical Sample Size: 100 to 1,000 participants.
Example: A health clinic might survey 300 patients to evaluate their satisfaction with a new treatment protocol.

Purpose: To collect data for scientific studies or academic research.
Typical Sample Size: 100 to 1,500 participants, depending on the study's scope and methodology.
Example: A study examining student performance might use a sample size of 600 students to ensure statistically significant results.

Purpose: To gauge public opinion on political issues or candidates.
Typical Sample Size: 1,000 to 2,000 participants.
Example: A national poll might survey 1,500 voters to predict election outcomes or public opinion on policies accurately.

Purpose: To evaluate a product’s usability, features, or market fit.
Typical Sample Size: 50 to 500 participants.
Example: To gather feedback on user experience and functionality for a new software product, a sample of 200 users might be surveyed.

Purpose: To assess attendees' experiences and gather feedback on events.
Typical Sample Size: 50 to 500 participants.
Example: After a conference, surveying 200 attendees can provide valuable insights into the event’s success and areas for improvement.

Adhering to best practices ensures that your survey or study yields reliable and accurate results when calculating sample size.

Here are some key best practices to follow:

Purpose: Clearly define your study's objectives to determine the appropriate sample size. The goals will guide your choices for confidence level and margin of error.

Common Levels: A 95% confidence level is typically used, but adjust this based on the importance of precision in your study. Higher confidence levels require larger sample sizes.

Precision vs. Cost: Decide on an acceptable margin of error. Smaller margins require larger sample sizes but provide more precise results.

Use 0.5 if Unknown: If the proportion of the population with the characteristic of interest is unknown, use 0.5. This maximizes the required sample size and ensures you cover all possible scenarios.

Apply Finite Population Correction: When working with smaller or finite populations, use the finite population correction formula to adjust the sample size for more accurate results.

Accuracy: To ensure accuracy, utilize established formulas or sample size calculators. Many online calculators are available for different types of surveys and studies.

Increase Sample Size: Increase the sample size by anticipating non-responses or incomplete data. This helps maintain the quality of your results.

Transparency: Document the assumptions and methods used in calculating sample size. This ensures transparency and allows others to understand and replicate your study.

Use our sample size calculator to enhance your survey results. For a larger pool of respondents, utilize Qatalyst Audiences to reach participants globally and gather responses from diverse individuals. Access 80 million+ respondents across 120+ countries through seamless integrations with leading panels like Cint, Lucid, Dynata, Purespectrum, Prolific, and Respondent.

What is a Sample Size Calculator?

A sample size calculator helps determine the number of observations or individuals needed in a survey or study to ensure accurate and reliable results. It considers factors like population size, confidence level, and margin of error.

What is considered a good sample size?

A good sample size depends on the type of survey. For market research, sample sizes typically range from 100 to 1,000 participants. A sample size of 200 to 1,000 is common for customer satisfaction surveys. Employee engagement surveys often range from 100 to 500 participants, depending on the organization's size.

How does population size affect sample size?

Population size affects sample size by influencing the required sample for accurate results. The required sample size remains relatively stable for large populations, while for smaller populations, the sample size needs to be adjusted to ensure accuracy and representativeness.