1. What is A/B Testing Significance?

The p-value in A/B testing helps determine whether the difference between two variants is statistically significant. It represents the probability of observing the test results (or more extreme) if there was no real difference between the variants.

2. How Does the Calculator Work?

The calculator uses the chi-square distribution:

Where:

• \( test\_stat \) — The calculated chi-square test statistic
• \( df \) — Degrees of freedom (typically 1 for 2×2 contingency tables)
• \( p \) — The resulting p-value (probability)

Explanation: The chi-square test compares observed frequencies with expected frequencies under the null hypothesis of no difference between groups.

3. Importance of p-value in A/B Testing

Details: A low p-value (typically <0.05) suggests that the observed differences are statistically significant and not due to random chance.

4. Using the Calculator

Tips: Enter your chi-square test statistic and degrees of freedom. For a standard 2×2 A/B test, degrees of freedom is 1.

5. Frequently Asked Questions (FAQ)

Q1: What is a good p-value threshold?
A: Typically 0.05 is used, but stricter thresholds (0.01) may be needed for multiple comparisons or high-stakes decisions.

Q2: How is the test statistic calculated?
A: For a 2×2 contingency table: \( \chi^2 = \sum \frac{(O-E)^2}{E} \), where O=observed, E=expected.

Q3: What if my p-value is borderline (e.g., 0.06)?
A: Consider the context, sample size, and effect size. You may need more data or a different statistical approach.

Q4: Can I use this for non-binary outcomes?
A: The chi-square test works for categorical data with more than two categories, but degrees of freedom will differ.

Q5: What are alternatives to chi-square test?
A: For small samples, Fisher's exact test. For continuous data, t-tests or ANOVA may be more appropriate.