ab-test-stats

To calculate statistical significance for A/B test results, you input visitor and conversion counts for your control and variant groups. The script computes the p-value and confidence level, validating whether observed conversion rate differences are real or random fluctuations.

Determining the required sample size for A/B testing involves running a power analysis with your minimum detectable effect and desired confidence level. This Skill calculates the exact number of visitors needed per group to ensure your data analysis is reliable.

Estimating the duration needed to run an A/B test requires your required sample size and expected daily traffic. This Skill calculates the estimated time your experiment needs to run to achieve statistical significance and reliable experimentation outcomes.

Yes, this A/B testing statistics tool requires SciPy, NumPy, and Click dependencies. You need a Python environment with these libraries installed to execute the scripts for significance testing, sample size calculation, and duration estimation.

Your A/B test conversion rate increase might not be statistically significant due to an insufficient sample size or short experiment duration. Use this Skill's power analysis to calculate required visitors and confirm if the observed difference is real or just chance.