Sample Size Calculator

Find required sample size or margin of error for surveys and experiments

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Sample Size Calculator

Compute the minimum number of necessary samples to meet the desired statistical constraints. Also find the margin of error for a given sample size.

Quick Navigation

📏 Find Sample Size 📐 Find Margin of Error

📏 Find Out The Sample Size

This calculator computes the minimum number of necessary samples to meet the desired statistical constraints.

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Use 50% if not sure

Leave blank if unlimited population size

📊 Result

With a confidence level of 95%, margin of error of 5%, and population proportion of 50%, you need a sample size of 385 samples.

Note: This calculation assumes a normal distribution and uses the standard formula for sample size determination. Larger sample sizes generally provide more accurate results but require more resources.

📐 Find Out the Margin of Error

This calculator gives out the margin of error or confidence interval of observation or survey.

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Leave blank if unlimited population size

Result

With a sample size of 100, confidence level of 95%, and population proportion of 60%, the margin of error is 9.8%.
Confidence Level: 95%
Sample Size: 100
Population Proportion: 60%
Margin of Error: Âą9.8%

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Confidence Interval

Calculate statistical intervals

📚 Understanding Sample Size and Statistics

Confidence Level

The confidence level is a measure of certainty regarding how accurately a sample reflects the population being studied within a chosen confidence interval. Common levels are 90%, 95%, and 99%.

Margin of Error

The margin of error is a way to express sampling error in a measurement or survey. It indicates the range of values within which the true population parameter likely falls.

Population Proportion

A population proportion describes a percentage of the value associated with a survey. Use 50% if you're unsure, as this provides the most conservative sample size estimate.

Sample Size Calculation

Sample size calculation is a statistical concept that involves determining the number of observations or replicates that should be included in a statistical sample. It is an important aspect of any empirical study requiring that inferences be made about a population based on a sample.

Key Considerations

  • Confidence Level: Higher confidence levels require larger sample sizes
  • Margin of Error: Smaller margins of error require larger sample sizes
  • Population Proportion: Proportions closer to 50% require larger sample sizes
  • Population Size: For large populations, the population size has minimal impact
  • Cost vs. Accuracy: Balance the cost of data collection with the required precision

Practical Applications

Sample size calculations are essential in market research, clinical trials, opinion polls, and quality control. Proper sample size ensures that your study has sufficient statistical power to detect meaningful differences or relationships while minimizing unnecessary costs and resource allocation.