Animal Sample Size Calculator
How many animals per group do you need? Run a power analysis for two means or two proportions, with built-in attrition adjustment and 3Rs guidance.
Plan your study, then track it
Manage study cohorts in Moustra
Once you know your target n, Moustra helps you track enrolment, exclusions, and attrition in real time — keeping your study on budget and your IACUC numbers accurate.
Try Moustra FreePower analysis results
Animals per group (enrolment)
70
140 animals total across both groups
Effect size
Cohen's d = 0.50
Completers needed per group
63
Before attrition inflation
Enrolment per group is inflated from 63 completers to account for 10% expected attrition.
3Rs — Reduction note
Using the smallest adequately powered sample size supports the 3Rs principle of Reduction and strengthens your IACUC protocol justification. This estimate is based on normal-approximation formulas; confirm the final number with a statistician before submission, especially for non-normal outcomes or complex designs.
How sample size and power analysis works
A sample size calculation answers one question: how many subjects do you need so that, if a real difference exists, you will reliably detect it? The answer depends on four quantities.
Alpha (significance level) is the probability of a false positive — concluding there is an effect when there is none. The conventional threshold is 0.05 (two-sided), meaning you accept a 5% false-alarm rate. Stricter studies use 0.01.
Power (1 − beta) is the probability of detecting a real effect — a power of 0.80 means you have an 80% chance of reaching statistical significance if the true effect is at least as large as you specified. Higher power requires more animals.
Effect size captures how different the groups are relative to natural variability. For continuous outcomes, Cohen's d = |mean1 − mean2| / SD. For binary outcomes, the absolute difference between proportions is used. Smaller effect sizes require larger samples.
For two means, the formula is approximately n = 2(z_alpha/2 + z_beta)² / d², where z values are the standard-normal quantiles corresponding to alpha and power. For two proportions, the numerator uses the pooled variance of both rates. In both cases, the result is rounded up to a whole number of animals.
Attrition inflation adjusts upward so that even after expected losses (deaths, exclusions, missing data) you still have enough completers to reach the required power. If you expect 10% dropout, enrol n / (1 − 0.10) animals per group.
How to use this calculator
- Choose your analysis type — select "Compare two means" for continuous outcomes (weight, volume, concentration) or "Compare two proportions" for binary outcomes (survival, response rate).
- Set alpha and power — 0.05 and 0.80 are the conventional defaults. Use 0.90 or 0.95 power when the cost of a false negative is high.
- Enter expected attrition — use your lab's historical loss rate or a conservative estimate (10–20% is common for rodent studies). The calculator inflates enrolment automatically.
- Enter your effect parameters — for means, provide both group means and the expected standard deviation (from pilot data or the literature). For proportions, enter the expected response rate in each group as a percentage.
- Read the results — the highlighted box shows animals to enrol per group and the total. Export a CSV for your IACUC submission or share the URL with your collaborators.
Frequently Asked Questions
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