Statsig N Calculator

Determine the required sample size (n) for A/B testing using the Statsig N Calculator. This tool helps you calculate the minimum number of participants needed to achieve statistical significance in your experiments.

What is Statsig N?

Statsig N refers to the sample size calculation for A/B testing and statistical experiments. The sample size (n) is the number of participants needed to detect a meaningful difference between two groups with a specified level of confidence.

Calculating the appropriate sample size is crucial for efficient experimentation. Too small a sample may lead to unreliable results, while too large a sample wastes resources. The Statsig N Calculator uses statistical power analysis to determine the optimal sample size based on your experiment's parameters.

How to Use the Calculator

Enter the baseline conversion rate (p1) - the current success rate of your control group.
Enter the minimum detectable effect (MDE) - the smallest difference you want to detect between the control and treatment groups.
Select the statistical power (1-β) - the probability of correctly detecting a true effect (typically 80% or 90%).
Select the significance level (α) - the probability of a false positive (typically 5% or 1%).
Click "Calculate" to determine the required sample size.

The calculator will display the minimum number of participants needed for each group to achieve the desired statistical power.

Formula

The sample size calculation for A/B testing is based on the following formula:

n = 2 * (Zα/2 + Zβ)² * p1 * (1 - p1) / MDE²

Where:

n = required sample size for each group
Zα/2 = z-score for the significance level (α)
Zβ = z-score for the statistical power (1-β)
p1 = baseline conversion rate
MDE = minimum detectable effect

This formula accounts for the variability in your data and ensures you have enough power to detect meaningful differences between groups.

Example Calculation

Suppose you have a website with a 10% conversion rate (p1 = 0.10). You want to detect a 5% improvement (MDE = 0.05) with 90% statistical power (1-β = 0.90) and a 5% significance level (α = 0.05).

Using the Statsig N Calculator:

Enter p1 = 0.10
Enter MDE = 0.05
Select Power = 90%
Select Significance = 5%
Click Calculate

The calculator will determine that you need approximately 1,224 participants in each group to detect a 5% improvement with 90% confidence.

Interpreting Results

The Statsig N Calculator provides several key outputs:

Sample Size per Group: The minimum number of participants needed for each group in your experiment.
Total Sample Size: The combined number of participants needed for both groups.
Power Analysis: A visualization showing how sample size affects your ability to detect effects of different sizes.

Use these results to plan your experiment, ensuring you have enough participants to achieve meaningful results while avoiding unnecessary costs.

Remember that sample size calculations are based on assumptions about your data. Actual results may vary based on real-world conditions and data quality.

FAQ

What is the difference between statistical power and significance level?

The significance level (α) is the probability of a false positive (Type I error), while statistical power (1-β) is the probability of correctly detecting a true effect (avoiding a Type II error). Higher power means you're less likely to miss a real effect.

How do I choose the minimum detectable effect?

The minimum detectable effect should be based on what difference would be meaningful or actionable for your business. Smaller effects require larger sample sizes.

What if my conversion rate is very low?

Very low conversion rates can result in very large sample size requirements. In such cases, consider whether the experiment is worth the cost or if you can improve the baseline conversion rate first.