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Sample Size Calculator Calculate required sample size for surveys based on confidence level, margin of error, and population.

Sample Size Calculator illustration
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Sample Size Calculator

Calculate required sample size for surveys based on confidence level, margin of error, and population.

1

Set Confidence Level

Choose your desired confidence level (e.g., 95%).

2

Enter Parameters

Set margin of error, population size, and expected proportion.

3

View Sample Size

See the required sample size with calculation details.

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What Is Sample Size Calculator?

The Sample Size Calculator determines how many respondents or observations you need for a statistically valid survey or study. It considers the confidence level (how certain you want to be), margin of error (acceptable range of error), population size (total group being studied), and expected proportion (anticipated result percentage). The calculator uses the standard sample size formula with finite population correction when a population size is provided. This tool is essential for researchers, marketers, and anyone designing surveys or experiments.

Why Use Sample Size Calculator?

  • Standard statistical formula with finite population correction
  • Multiple confidence levels from 80% to 99.9%
  • Optional population size for finite population adjustment
  • Shows z-score and all calculation parameters

Common Use Cases

Market Research

Determine survey size for customer satisfaction or product research.

Academic Research

Calculate sample size for thesis research or scientific studies.

Quality Control

Determine inspection sample sizes for manufacturing processes.

Public Opinion Polls

Plan polling sample sizes for elections or policy research.

Technical Guide

The sample size formula for infinite population is: n₀ = (Z² × p × (1-p)) / E², where Z is the z-score for the confidence level, p is the expected proportion (0.5 for maximum), and E is the margin of error. For finite populations, apply the correction: n = n₀ / (1 + (n₀-1)/N), where N is the population size. Z-scores: 80% → 1.282, 90% → 1.645, 95% → 1.960, 99% → 2.576. Using p = 0.5 gives the most conservative (largest) sample size. The finite population correction becomes significant when the sample is more than 5% of the population.

Tips & Best Practices

  • 1
    Use p = 50% when you are unsure of the expected proportion — this gives the largest sample
  • 2
    Doubling confidence does not double sample size — the relationship is non-linear
  • 3
    Halving the margin of error quadruples the required sample size
  • 4
    For populations over 100,000, the finite correction has minimal effect

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Frequently Asked Questions

Q What is a good sample size?
It depends on your requirements. For a 95% confidence level with 5% margin of error, you need about 385 respondents for a large population. Smaller margins of error require larger samples.
Q What confidence level should I use?
95% is the most common standard in research. Use 99% for critical decisions or medical research. 90% may suffice for preliminary or exploratory studies.
Q Does population size matter?
For large populations (>100,000), the required sample size barely changes. The finite population correction only matters when sampling a significant portion of the population.
Q What is margin of error?
Margin of error is the range within which the true population value is expected to fall. A 5% margin means results could be ±5% from the reported value.
Q Why use 50% for expected proportion?
Using 50% maximizes the required sample size, ensuring your sample is large enough regardless of the actual result. If you have a reasonable estimate, using it reduces the required sample.

About This Tool

Sample Size Calculator is a free online tool by FreeToolkit.ai. All processing happens directly in your browser — your data never leaves your device. No registration or installation required.