Which statistical method determines the minimum sample size required for an A/B test to achieve statistically significant results with 95% confidence?

Sample size calculation is the statistical method used to determine the minimum sample size required for an A/B test to achieve statistically significant results with 95% confidence. Sample size calculation involves using a formula that considers several factors: the desired statistical power (typically 80%), the significance level (alpha, typically 5% for 95% confidence), the expected effect size (the minimum difference you want to detect), and the standard deviation of the data. The statistical power represents the probability of correctly rejecting the null hypothesis (i.e., detecting a real effect when one exists). The significance level (alpha) represents the probability of incorrectly rejecting the null hypothesis (i.e., concluding there is an effect when there isn't). The effect size is the magnitude of the difference between the two groups (A and B) that you want to be able to detect. A larger effect size requires a smaller sample size, while a smaller effect size requires a larger sample size. The standard deviation measures the variability of the data. Higher variability requires a larger sample size. By plugging these values into the appropriate formula, one can determine the minimum sample size needed to achieve statistically significant results with the desired level of confidence. There are also online calculators and statistical software packages that can perform this calculation automatically.