Stat Trek

Teach yourself statistics

Stat Trek

Teach yourself statistics

Sample Size Calculator

The Sample Size Calculator guides you step-by-step to find the right sample design for your research. Use the calculator to create powerful, cost-effective survey sampling plans.

  • Find the optimum design (most precision, least cost).
  • See how sample size affects cost and precision.
  • Compare different survey sampling methods.
  • Assess statistical power and Type II errors.

In each section, provide the data requested. The calculator does the rest. It crunches numbers and generates an easy-to-understand report that summarizes key findings, describes the analysis, and documents calculator inputs.

Describe the Research

The first step in using this calculator is to describe the research you are conducting.

  • Purpose of research: Most surveys are designed to estimate a population parameter. Some surveys also test a hypothesis about that parameter. In the dropdown box below, indicate whether your survey is focused only on estimation or whether it also includes a hypothesis test.

  • Sample statistic: Most surveys use a sample statistic to estimate a population parameter. This calculator can work with two types of statistics: a mean score and a proportion. From the dropdown box below, select the sample statistic that you will use.

  • Population parameter: Most surveys use a sample statistic to estimate a population parameter. From the dropbox below, identify the population parameter that you want to estimate.

Define the Output

In this section, specify the output that you desire from the Sample Size Calculator - your main goal plus any optional analyses.

Main goal

Optional analyses (select all that apply)

Define Statistical Constraints

Please provide the following additional information:

  • Confidence level: A confidence level refers to the percentage of all possible samples that can be expected to include the true population parameter. For example, suppose all possible samples were selected from the same population, and a confidence interval were computed for each sample. A 95% confidence level implies that 95% of the confidence intervals would include the true population parameter.
  • Confidence level

  • Margin of error: The margin of error is a measure of sampling error in a survey result. The bigger the margin of error, the less confidence a researcher can place in the survey result. To be meaningful, a confidence level should be reported along with the margin of error. For example, a margin of error of 3 percent based on a 95% confidence level is more impressive than a margin of error of 3 percent based on a 90% confidence level.

Describe the Population and Sample

Specify population and sample properties. If you don't know the exact values for these inputs, estimate. Base your estimate on the best information that you have - personal experience with similar studies, previous research by others, or even subjective judgment.

Write Report

To analyze data you've entered and write a report summarizing key findings, click the Calculate button.

Image indicating that a problem has occurred


Oops! Something went wrong.