Statistical Tools for Business Decisions
Practical calculators that bridge the gap between statistical rigor and business reality. Each tool includes context on when to use it, its limitations, and how to interpret results for real-world impact.
Featured Tool
A/B Test Sample Size Calculator
Calculate the sample size needed for your A/B tests with business context in mind. Understand the trade-offs between statistical power, practical significance, and time-to-decision.
Key considerations:
- •Why p<0.05 isn't always the right threshold
- •Balancing speed vs. confidence in decision-making
- •When to stop tests early (and when not to)
Sample Size Calculator
With business context
Statistical Power
Don't miss real effects
Statistical Power Calculator
Calculate the probability of detecting a true effect in your experiments. Find the sweet spot between sample size, effect size, and confidence.
Essential for:
- •Avoiding false negatives in critical tests
- •Right-sizing experiments for your traffic
- •Understanding detection limits upfront
More Tools Coming Soon
Bayesian A/B Test Analyzer
Move beyond p-values to probability of superiority. Perfect for continuous monitoring and making decisions with incomplete data.
Minimum Detectable Effect Calculator
Given your traffic and timeline, what's the smallest improvement you can reliably detect? Essential for prioritizing experiments by potential impact.
Sequential Test Boundary Calculator
Design tests that can be stopped early without inflating false positive rates. Ship faster without sacrificing statistical validity.
Why Business Context Matters
Statistical Significance ≠ Business Impact
A p-value of 0.06 might be "not significant" in academia, but a 94% confidence in a 10% revenue lift is often worth acting on in business. These tools help you make pragmatic decisions.
Time is Money
Waiting for 95% statistical power might mean missing a quarter's worth of gains. These calculators help you balance confidence with speed-to-market.
Risk Tolerance Varies
A 5% false positive rate might be acceptable for a UI change but not for a pricing algorithm. Context determines the appropriate statistical thresholds.
Practical vs. Statistical Significance
With enough data, you can find statistical significance for tiny effects. These tools emphasize minimum detectable effects that actually matter.
Better Decisions Through Better Tools
Stop over-engineering experiments and start shipping with confidence. These tools help you find the sweet spot between rigor and practicality.
Let's Chat About Your Experiments