Power Analysis Calculator (Biology)

Statistical power is the probability of correctly rejecting a false null hypothesis (avoiding Type II error). In biology, this means the likelihood of detecting a true biological effect when it exists. Higher power (typically 80% or more) reduces the chance of missing important biological differences.

Choose continuous for measurable outcomes like weight, enzyme activity, or gene expression levels. Choose dichotomous for yes/no outcomes like survival/death, presence/absence of a trait, or treatment success/failure. Continuous endpoints generally require smaller sample sizes for the same power.

Alpha = 0.05 is standard for most biological research. Use 0.01 for more stringent testing when false positives are costly. For exploratory studies or when multiple comparisons are involved, consider adjusting alpha using Bonferroni correction or false discovery rate methods.

Effect size measures the magnitude of difference between groups, independent of sample size. Cohen's d values of 0.2, 0.5, and 0.8 represent small, medium, and large effects respectively. Larger effect sizes require smaller sample sizes to detect with adequate power.

Allocation ratio determines how subjects are divided between groups. Equal allocation (1:1) is most efficient. Unequal ratios may be necessary due to cost, ethics, or practical constraints, but typically require larger total sample sizes to maintain the same power.

Large sample size requirements may indicate a small effect size, high variability, or stringent power requirements. Consider increasing alpha (if appropriate), reducing desired power to 70-80%, or improving experimental design to reduce variability through better controls or blocking.

Yes, always inflate your calculated sample size to account for expected dropouts, non-compliance, or data loss. For animal studies, plan for 10-20% loss. For human studies, dropout rates can be 20-30% or higher depending on study duration and complexity.