Design Effect & Effective Sample Size Calculator
See how much clustering costs you in statistical power. Enter your sample size, the average responses per cluster and the intra-cluster correlation.
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Why clustered samples count for less
When you collect responses in clusters, such as several classrooms, clinics or stores, people within a cluster tend to be similar to each other. That similarity means a clustered sample carries less information than a simple random sample of the same size, so its results are less precise than the raw count of responses suggests.
The formulas
The design effect is 1 + (m − 1) × ICC, where m is the average cluster size and ICC is the intra-cluster correlation. The effective sample size is your actual sample size divided by the design effect. It is the size of a simple random sample that would give the same precision, and it is the number to use when planning margins of error.
Frequently asked questions
What ICC should I use?
If you have no prior data, values between 0.01 and 0.05 are common for large surveys, though tightly clustered outcomes can be higher. Use an estimate from a similar past study when you can.
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