Calculate the minimum sample size required for biological experiments and clinical studies to achieve statistical significance with proper power analysis.
Sample Size Per Group
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Total Sample Size
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Effect Size (Cohen's d)
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Minimum Detectable Difference
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Statistical power is the probability of correctly detecting a true effect when it exists. 80% power means there's an 80% chance of finding a significant result if the effect is real. This is a widely accepted standard that balances the risk of missing true effects with practical sample size constraints.
The significance level represents the probability of falsely detecting an effect (Type I error). Lower α values (like 0.01 vs 0.05) require larger sample sizes because you need stronger evidence to declare statistical significance. Most biological studies use α = 0.05 as the standard.
Effect size measures the magnitude of difference between groups. Cohen's d is commonly used for continuous variables, where 0.2 is small, 0.5 is medium, and 0.8 is large. You can estimate effect size from pilot data, previous studies, or determine the minimum clinically meaningful difference.
Two-sided tests are more conservative and widely accepted in biological research because they test for differences in either direction. One-sided tests are used only when you have strong theoretical reasons to expect an effect in one specific direction and require smaller sample sizes.
The allocation ratio determines the relative sizes of your groups. A 1:1 ratio is most efficient for detecting differences, but unequal allocation might be necessary due to practical constraints like cost, availability of subjects, or ethical considerations.
Continuous endpoints are measured on a scale (weight, blood pressure, enzyme activity) while dichotomous endpoints are binary outcomes (success/failure, alive/dead, disease/no disease). Continuous endpoints typically require smaller sample sizes for the same power.
The calculated sample size assumes all subjects complete the study. You should inflate your target enrollment by 10-20% (or your expected dropout rate) to ensure adequate power after accounting for subjects lost to follow-up or protocol violations.
This calculator is designed for t-tests and proportion comparisons. For non-parametric tests, multiply the result by 1.05-1.15. For survival analysis or more complex designs, specialized software or consultation with a biostatistician is recommended.