How Safe Tests Reduce Sample Sizes Without Compromising Statistical Validity

This article compares Python implementations of the safe t-test and safe proportion test with their classical counterparts, the t-test and χ² test. By optimizing algorithms, introducing binary search and vectorized operations, and adapting batch size flexibility, the safe tests often require fewer samples for valid conclusions. However, achieving the same statistical power can demand more data, making them ideal for scenarios where early stopping is valuable. Results show safe tests can save time and resources while maintaining accuracy, offering a compelling alternative for data scientists.