Free Split Test Calculator
Analysis Methods Explained
The traditional statistical method that answers: "If there were no real difference between A and B, what's the chance we'd see these results by luck?"
- Uses p-values and confidence levels (95% confidence is standard)
- Tells you if results are "statistically significant"
- More reliable with larger sample sizes (1000+ visitors)
- Familiar to most analysts and stakeholders
A more intuitive approach that directly answers: "What's the probability that B is actually better than A?"
- Shows direct probability of B being better
- Works well even with smaller sample sizes
- Takes into account uncertainty in the data
- Provides continuous updates as data comes in
Using both methods gives you the most complete picture:
- See if results are significant (Frequentist)
- Understand the probability of improvement (Bayesian)
- Make more confident decisions
- Better understand the risks involved
- Frequentist: When you need a clear yes/no decision and have large sample sizes
- Bayesian: When you want probability-based decisions or have smaller samples
- Both: When you want the most comprehensive analysis (recommended)
Revenue Data Analysis
Revenue data provides deeper insights into the true business impact of your test variants beyond just conversion rates.
- A variant with lower conversions might actually generate more total revenue if its customers make larger purchases
- Helps identify which variant is more profitable, not just which converts better
- Shows revenue per visitor for each variant, a key business metric
The calculator performs two separate statistical tests:
- Conversion Rate Test: Uses a Z-test to determine if the difference in conversion rates is statistically significant
- Revenue Test: Uses a t-test to determine if the difference in revenue per visitor is statistically significant
Each test provides its own confidence level, helping you understand:
- How confident you can be about the conversion rate difference
- How confident you can be about the revenue difference
- Whether the results align or conflict between metrics
Consider two variants:
- Variant A: 10% conversion rate, $50 per conversion = $5.00 per visitor
- Variant B: 8% conversion rate, $75 per conversion = $6.00 per visitor
- While Variant A has better conversions, Variant B generates 20% more revenue
The calculator will show:
- Statistical confidence for the conversion rate difference
- Statistical confidence for the revenue per visitor difference
- Whether these differences are significant enough to make a decision
- When testing pricing strategies or purchase flows
- If different variants might attract different customer segments
- When optimizing for revenue is as important as conversion rate
- To understand the full economic impact of your test
Sample Size Ratio
This shows how evenly your visitors are split between variants A and B.
- A ratio of 1.00 means you have equal visitors in both groups (ideal)
- Numbers far from 1.00 mean uneven split, which can affect reliability
- Try to keep this number between 0.8 and 1.2 for best results
Minimum Detectable Effect
The smallest improvement you can reliably detect with your current sample size.
- Smaller numbers are better - they mean you can detect subtle differences
- Large numbers mean you can only detect big changes
- To decrease MDE, increase your sample size
Power Analysis
Shows how likely you are to detect a real difference if one exists.
- Higher is better - aim for at least 80%
- Low power means you might miss real improvements
- To increase power, get more visitors or look for bigger improvements
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