Our sample size calculator:

md.asif.islam.1

Uses Monte Carlo simulation to estimate the required sample size under FDR adjustment, but there is no simple closed-form solution. Accepts multiple metrics and variants to solve complex sample size calculations. Regardless of the number of variants, metadata for the decision metric is only requested once. Each treatment variant is assumed to have the same MDE. Calculating sample sizes for multiple comparisons The calculator uses the statsmodels Python package to calculate the required sample sizes when there is only one hypothesis test.


Apply calculations (such as the delta method) to Nurse Email List calculate the necessary parameters. Adapting the Benjamini Hochberg adjustment to a list of Bolivian phone numbers for multiple hypothesis testing. Because the procedure determines how to adjust the p-values ​​by sorting them, there is no closed-form formula that solves for sample size in this scenario. We can adjust the family error rate by the Bonferroni adjustment (alpha divided by the number of hypothesis tests) because it is a more conservative adjustment than the BenjaminiHochberg adjustment.




However, using this conservative adjustment almost guarantees that the experiment will be overwhelmed for a longer period of time, thus slowing down the experiment. The calculator follows the steps below to search for the minimum sample size required under the Benjamini Hochberg adjustment. Step 1: Generate p-values ​​Each indicator class has a class method for calculating p-values ​​based on sampled test statistics. If the null hypothesis is true, sample_size draws the number of p-values ​​Uniform from the p-value distribution under the null hypothesis; otherwise, the sample is drawn from the test statistic distribution under the alternative hypothesis.