Enter your population size and desired sample size to calculate the sampling interval (k) for your systematic sampling design. Adjust the confidence level and margin of error to fine-tune your required sample size, then use the resulting interval to select every k-th element from your population list.
Sampling Interval (k)
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Required Sample Size
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Population Coverage
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Random Start Range (1 to k)
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Systematic sampling is a probability sampling method where you select every k-th element from a population list after choosing a random starting point between 1 and k. It is simpler to execute than simple random sampling and produces evenly spread samples across the population.
The sampling interval k is calculated by dividing the total population size (N) by the required sample size (n): k = N / n. For example, if your population is 10,000 and your required sample size is 370, you would select every 27th individual.
Sample size is the number of individuals selected from a population to represent it in a study or survey. A larger sample size generally reduces the margin of error and increases the reliability of your results, but also increases cost and effort.
A 95% confidence level is the most common choice in research and surveys, meaning that if you repeated your study 100 times, 95 of those results would fall within your margin of error. Use 99% for high-stakes decisions or 90% when resources are limited.
The margin of error is the maximum expected difference between your sample result and the true population value. A margin of error of ±5% means your survey result could be 5 percentage points above or below the actual figure in the population.
When you are unsure of the expected proportion of your population that holds a certain attribute, using 50% gives the most conservative (largest) sample size estimate. This ensures your study is adequately powered regardless of the actual proportion.
A z-score is the number of standard deviations from the mean corresponding to a given confidence level in a normal distribution. For a 95% confidence level, the z-score is 1.96; for 99% it is 2.576. The z-score is used in the sample size formula to determine how wide your confidence interval will be.
Systematic sampling works well when your population list has no hidden periodic pattern that matches your sampling interval. If there is a cyclical pattern in the list (e.g., every 7th record is a weekend), systematic sampling could introduce bias. In such cases, simple random sampling or stratified sampling may be more appropriate.