Online Sampling Tool

A statistical planning tool that helps researchers determine required sample size or margin of error for surveys, using population size, confidence level, and estimated proportion.

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About This Tool

This tool provides statistically grounded estimates for survey design by calculating the required sample size or the margin of error given population size, confidence level, and an estimated proportion. It serves researchers, market analysts, and students who need quick, defensible sampling guidance during study planning.

Conceptually it uses standard proportion-based formulas. For a given margin of error E and confidence level, n0 = (Z^2 * p(1-p)) / E^2, and if a finite population size N is supplied, n = n0 / (1 + (n0 - 1)/N). The tool also supports computing E for a given n, N, and p. Defaults use p = 0.5 for a conservative estimate.

Outputs include the recommended sample size, the corresponding margin of error, the Z-score for the chosen level, and whether finite population correction was applied. The approach is explicit about assumptions and returns rounded values to avoid under- or over-sampling. It is suitable for quick feasibility checks, budget planning, and educational uses.

Why it differs: it presents domain-specific calculations with clear inputs and outputs, avoiding design effects, weighting, or multi-stage designs by default; users can adjust inputs to reflect these complexities manually if needed.

How to Use

  1. Provide inputs: population_size (optional), confidence_level (90/95/
  2. , and either margin_of_error (percent) or set mode to calculate sample size. Set estimated_proportion_p (default
  3. .
  4. Choose mode: 'sample_size' to compute n for your MOE, or 'margin_of_error' to find E for a given n.
  5. Run calculation to obtain outputs: recommended sample size, MOE, Z-score, and FPC flag.
  6. Review outputs and adjust for real-world constraints (response rate, design effects) by inflating n if needed.
  7. Export or save the results for documentation.
How to use online sampling tool

Frequently Asked Questions

Find Quick Answers

What is this tool best used for?
It helps plan surveys by providing a defensible estimate of the number of respondents required for a specified accuracy or the feasible margin of error for a given sample size. Outputs include the recommended n, the corresponding MOE, the Z-score for the chosen confidence level, and whether finite population correction was applied.
How does finite population correction affect results?
When population size N is provided, the calculation reduces the required sample size compared to an infinite population, especially for small N. The MOE at a fixed n also decreases with smaller N, reflecting reduced variability in a small population.
What if I don't know p?
Use p = 0.5 as a conservative default, which yields the largest required sample size for a given E and Z. If prior information suggests a different p, adjust accordingly to obtain a tighter estimate.
What sampling assumptions are made?
The tool assumes simple random sampling with no weighting or clustering by default. It does not model complex designs, response biases, or post-stratification unless inputs are adjusted to reflect those factors.

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