statistical-power-calculator

To calculate statistical power and sample size for a t-test, input your desired effect size, significance level, and target power. The calculator uses scipy and statsmodels to return the exact minimum sample size needed per group to detect the specified effect.

Statistical power analysis determines the probability of detecting a true effect during hypothesis testing. You need it for experimental design to ensure your research is adequately powered, preventing false negatives and calculating the minimum sample size required for meaningful results.

Yes, you can estimate effect size for ANOVA and chi-square tests using Python. The calculator supports multiple test types, allowing you to estimate the minimum detectable effect size given your constraints for sample size and statistical power.

This power calculator works for proportion tests and clinical trial planning by utilizing Python libraries like statsmodels. You can determine the exact number of participants needed to achieve specific power levels, such as 80%, given a 0.05 significance level for treatment effects.

The sample size you need for 80% power in hypothesis testing depends on your expected effect size and significance level. By providing these parameters, the calculator determines the minimum number of observations required per group to reliably detect true effects.