A/B Test Significance & Lift Calculator

Calculate statistical significance and lift for A/B tests. Compare baseline vs variant using conversion rates or continuous metrics, compute p-values, confidence intervals, and determine if your experiment shows meaningful results.

For educational purposes only — not for clinical, medical, or regulatory decisions

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A/B Test Significance Calculator

Enter your experiment data to calculate statistical significance, lift, p-values, and confidence intervals. Supports both conversion rate tests and continuous metric comparisons.

Conversion TestsMean ComparisonsLift Analysisp-values

Last Updated: November 1, 2025

Understanding A/B Test Significance and Lift: Essential Calculations for Data-Driven Decision Making

A/B testing (also called split testing) is a method of comparing two versions of something to determine which performs better. In digital experimentation, you randomly assign users to either a control group (baseline/original) or a treatment group (variant/new version) and measure a key metric like conversion rate or revenue. Understanding A/B testing is crucial for students studying data science, statistics, marketing analytics, and business intelligence, as it explains how to compare groups, determine statistical significance, and make data-driven decisions. A/B testing concepts appear in virtually every analytics protocol and are foundational to understanding experimentation.

Key components of A/B testing include: (1) Baseline (control)—the original version being tested, (2) Variant (treatment)—the new version being tested, (3) Conversion rate—proportion of users who convert (proportion mode), (4) Mean metric—average value of a continuous metric (mean mode), (5) Statistical significance—whether observed differences are likely real, (6) Lift—relative improvement of variant over baseline. Understanding these components helps you see why each is needed and how they work together.

Proportion tests compare binary outcomes (conversion or not) between groups. Examples include click-through rates, sign-up rates, purchase conversion, and form completion rates. The test compares conversion rates p_A and p_B using a pooled standard error. Understanding proportion tests helps you see how to compare conversion rates and why they're fundamental to digital marketing and product optimization.

Mean tests compare continuous numeric outcomes between groups. Examples include revenue per user, time on page, number of items purchased, and session duration. The test compares means m_A and m_B using a Welch-style standard error. Understanding mean tests helps you see how to compare continuous metrics and why they're essential for revenue and engagement analysis.

Statistical significance indicates whether observed differences are likely real (not due to chance). A p-value below your chosen threshold (alpha, often 0.05) means the result is statistically significant. However, statistical significance doesn't mean the effect is large or practically important—always consider effect size (lift) alongside p-values. Understanding significance helps you see how to interpret test results and why both statistical and practical significance matter.

This calculator is designed for educational exploration and practice. It helps students master A/B testing by calculating p-values, confidence intervals, lift percentages, and determining statistical significance. The tool provides step-by-step calculations showing how z-tests work for both proportion and mean comparisons. For students preparing for data science exams, statistics courses, or analytics labs, mastering A/B testing is essential—these concepts appear in virtually every analytics protocol and are fundamental to data-driven decision making. The calculator supports comprehensive analysis (proportion and mean modes, one-sided and two-sided tests), helping students understand all aspects of A/B testing.

Critical disclaimer: This calculator is for educational, homework, and conceptual learning purposes only. It helps you understand A/B testing theory, practice significance calculations, and explore how statistical tests work. It does NOT provide instructions for actual business decisions, which require proper training, validated experimentation platforms, expert statistical review, and adherence to best practices. Never use this tool to determine actual business decisions, product changes, or marketing campaigns without proper statistical review and validation. Real-world A/B testing involves considerations beyond this calculator's scope: peeking and early stopping, multiple comparisons correction, sequential testing, sample size planning, practical significance, business context, and replication. Use this tool to learn the theory—consult trained professionals and validated platforms for practical applications.

Understanding the Basics of A/B Test Significance

What Is A/B Testing and Why Does Statistical Significance Matter?

A/B testing compares a baseline (control) against a variant (treatment) to detect meaningful differences. Statistical significance indicates whether observed differences are likely real (not due to chance). Understanding significance helps you see why it's fundamental to data-driven decision making and experimentation.

How Do You Calculate Conversion Rates for Proportion Tests?

Conversion rates are calculated as: p = Conversions / Visitors. For example, if 100 out of 1000 visitors convert, p = 100/1000 = 0.10 (10%). Understanding this helps you see how conversion rates are computed and why they're fundamental to proportion tests.

How Do You Calculate Standard Error for Proportion Tests?

For the test statistic (pooled): SE_pooled = √(p_pooled × (1 - p_pooled) × (1/n_A + 1/n_B)), where p_pooled = (c_A + c_B) / (n_A + n_B). For confidence intervals (unpooled): SE = √((p_A × (1 - p_A)) / n_A + (p_B × (1 - p_B)) / n_B). Understanding this helps you see how standard errors are calculated for proportion tests.

How Do You Calculate Standard Error for Mean Tests?

Standard error for mean tests (Welch-style) is calculated as: SE = √((s_A² / n_A) + (s_B² / n_B)), where s_A and s_B are standard deviations, n_A and n_B are sample sizes. Understanding this helps you see how standard errors are calculated for mean tests.

How Do You Calculate the Test Statistic (z-score)?

The test statistic is calculated as: z = (Difference) / SE, where Difference = p_B - p_A (proportion) or m_B - m_A (mean), and SE is the appropriate standard error. Understanding this helps you see how z-scores quantify the difference relative to variability.

How Do You Calculate p-Value from z-Score?

For two-sided tests: p-value = 2 × (1 - Φ(|z|)), where Φ is the standard normal CDF. For one-sided tests: p-value = 1 - Φ(z) if z ≥ 0, or p-value = Φ(z) if z < 0. Understanding this helps you see how p-values are calculated from z-scores.

How Do You Calculate Relative Lift?

Relative lift is calculated as: Lift (%) = ((Variant / Baseline) - 1) × 100. For example, if baseline = 10% and variant = 12%, lift = ((12/10) - 1) × 100 = 20%. Understanding this helps you see how lift quantifies relative improvement.

How to Use the A/B Test Significance & Lift Calculator

This interactive tool helps you analyze A/B test results by calculating statistical significance, lift, confidence intervals, and determining winners. Here's a comprehensive guide to using each feature:

Step 1: Choose Test Mode

Select the type of metric you're testing:

Proportion Mode

Select for binary outcomes (conversion or not). Enter visitors and conversions for both baseline and variant groups.

Mean Mode

Select for continuous metrics. Enter mean, standard deviation, and sample size for both groups.

Step 2: Enter Test Data

Input your experimental data:

Baseline (Control) Data

For proportion: enter visitors and conversions. For mean: enter mean, standard deviation, and sample size.

Variant (Treatment) Data

Enter corresponding data for the variant group. Ensure sample sizes are adequate for reliable results.

Labels

Optionally customize group labels (e.g., "Control" vs "Treatment", "Original" vs "New Design").

Step 3: Configure Statistical Parameters

Set your significance level and test type:

Alpha (Significance Level)

Enter your chosen significance level (typically 0.05 for 95% confidence). Lower alpha (e.g., 0.01) is more conservative.

Test Type

Select "Two-sided" to detect any difference (positive or negative), or "One-sided" to test if variant is better than baseline.

Example: Proportion test with 1000 visitors each, 100 vs 120 conversions

Input: Baseline: 1000 visitors, 100 conversions; Variant: 1000 visitors, 120 conversions; Alpha: 0.05, Two-sided

Output: Baseline: 10%, Variant: 12%, Lift: 20%, p-value: 0.045, Significant: Yes, Winner: Variant

Explanation: Calculator computes conversion rates, calculates z-score, determines p-value, checks significance, identifies winner.

Step 4: Calculate and Review Results

Click "Calculate" to get your results:

View Calculation Results

The calculator shows: (a) Conversion rates or means for both groups, (b) Absolute difference and relative lift, (c) Test statistic (z-score) and p-value, (d) Confidence interval for the difference, (e) Statistical significance determination, (f) Winner identification, (g) Summary and caveats.

Tips for Effective Use

Ensure adequate sample sizes—small samples may fail to detect real effects (false negatives).
Use two-sided tests unless you have strong prior belief that variant can only be better.
Consider both statistical significance (p-value) and practical significance (lift) when making decisions.
Avoid peeking at results repeatedly—this inflates false positive rates.
Remember that significance doesn't guarantee future performance—always consider replication.
All calculations are for educational understanding, not actual business decisions.

Formulas and Mathematical Logic Behind A/B Test Significance

Understanding the mathematics empowers you to calculate significance on exams, verify calculator results, and build intuition about statistical testing.

1. Fundamental Relationship: Conversion Rate Calculation

p = Conversions / Visitors

Where:
p = conversion rate
Conversions = number of conversions
Visitors = total number of visitors

Key insight: Conversion rate is simply the proportion of visitors who convert. For example, 100 conversions out of 1000 visitors = 0.10 (10%). Understanding this helps you see how conversion rates are computed.

2. Pooled Proportion for Test Statistic

p_pooled = (c_A + c_B) / (n_A + n_B)

Where c_A, c_B = conversions, n_A, n_B = visitors

Example: (100 + 120) / (1000 + 1000) = 220/2000 = 0.11 (11%)

3. Standard Error for Proportion Test (Pooled)

SE_pooled = √(p_pooled × (1 - p_pooled) × (1/n_A + 1/n_B))

This is used for the test statistic calculation

Example: √(0.11 × 0.89 × (1/1000 + 1/1000)) ≈ 0.014

4. Standard Error for Confidence Interval (Unpooled)

SE = √((p_A × (1 - p_A)) / n_A + (p_B × (1 - p_B)) / n_B)

This is used for confidence intervals

Example: √((0.10 × 0.90)/1000 + (0.12 × 0.88)/1000) ≈ 0.014

5. Test Statistic (z-score)

z = (p_B - p_A) / SE_pooled

For proportion tests, or z = (m_B - m_A) / SE for mean tests

Example: (0.12 - 0.10) / 0.014 ≈ 1.43

6. p-Value Calculation

Two-sided: p = 2 × (1 - Φ(|z|))

One-sided: p = 1 - Φ(z) if z ≥ 0, or p = Φ(z) if z < 0

Where Φ is the standard normal CDF

Example: z = 1.43, two-sided → p = 2 × (1 - 0.9236) ≈ 0.153

7. Worked Example: Proportion Test with Significance

Given: Baseline: 1000 visitors, 100 conversions; Variant: 1000 visitors, 120 conversions; Alpha: 0.05, Two-sided

Find: Statistical significance and lift

Step 1: Calculate conversion rates

p_A = 100/1000 = 0.10 (10%), p_B = 120/1000 = 0.12 (12%)

Step 2: Calculate pooled proportion

p_pooled = (100 + 120) / (1000 + 1000) = 0.11

Step 3: Calculate standard error (pooled)

SE = √(0.11 × 0.89 × (1/1000 + 1/1000)) ≈ 0.014

Step 4: Calculate test statistic

z = (0.12 - 0.10) / 0.014 ≈ 1.43

Step 5: Calculate p-value

p = 2 × (1 - Φ(1.43)) ≈ 2 × 0.0764 ≈ 0.153

Step 6: Determine significance

p = 0.153 > 0.05 (alpha), so NOT significant

Step 7: Calculate lift

Lift = ((0.12/0.10) - 1) × 100 = 20%

Practical Applications and Use Cases

Understanding A/B test significance is essential for students across data science and statistics coursework. Here are detailed student-focused scenarios (all conceptual, not actual business decisions):

1. Homework Problem: Calculate p-Value from Test Data

Scenario: Your statistics homework asks: "Calculate the p-value for an A/B test with baseline: 1000 visitors, 100 conversions; variant: 1000 visitors, 120 conversions." Use the calculator: enter proportion mode, add data. The calculator shows: p-value ≈ 0.153. You learn: how to calculate pooled proportion, standard error, z-score, and p-value. The calculator helps you check your work and understand each step.

2. Lab Report: Understanding Statistical vs Practical Significance

Scenario: Your data science lab report asks: "A test shows 20% lift with p = 0.15. Is this significant?" Use the calculator: enter the data. The calculator shows: Not statistically significant (p > 0.05), but 20% lift is practically meaningful. Understanding this helps explain why statistical significance (p-value) and practical significance (lift) are different concepts. The calculator makes this relationship concrete—you see exactly how p-value and lift relate.

3. Exam Question: Compare One-Sided vs Two-Sided Tests

Scenario: An exam asks: "How does p-value change for one-sided vs two-sided tests?" Use the calculator: try both test types with same data. The calculator shows: One-sided p-value is half of two-sided (when variant is better). This demonstrates how test type affects p-value.

4. Problem Set: Interpret Confidence Intervals

Scenario: Problem: "A 95% confidence interval for difference is [0.01, 0.03]. Interpret this." Use the calculator: enter data to see confidence interval. The calculator shows: We're 95% confident the true difference is between 1% and 3%. This demonstrates how confidence intervals convey uncertainty about effect size.

5. Research Context: Understanding Why A/B Testing Matters

Scenario: Your data science homework asks: "Why is A/B testing important for data-driven decisions?" Use the calculator: explore different scenarios and their significance. Understanding this helps explain why A/B testing enables evidence-based decisions, reduces guesswork, quantifies improvements, and supports optimization. The calculator makes this relationship concrete—you see exactly how A/B testing quantifies differences and supports decision making.

Common Mistakes in A/B Test Significance Calculations

A/B testing problems involve statistical calculations, significance testing, and interpretation that are error-prone. Here are the most frequent mistakes and how to avoid them:

1. Using Wrong Standard Error Formula

Mistake: Using unpooled SE for test statistic or pooled SE for confidence interval, leading to wrong z-scores and p-values.

Why it's wrong: Test statistic uses pooled SE (assuming null hypothesis), confidence interval uses unpooled SE (for actual difference). Using wrong formula gives wrong z-scores. For example, using unpooled SE for test statistic gives wrong z-score.

Solution: Always remember: Test statistic = pooled SE, Confidence interval = unpooled SE. The calculator uses correct formulas—observe it to reinforce SE selection.

2. Confusing Statistical Significance with Practical Importance

Mistake: Assuming statistical significance (p < 0.05) means the effect is large or practically important.

Why it's wrong: Statistical significance only means the effect is likely real (not due to chance). A tiny effect can be significant with large samples, while a large effect may not be significant with small samples. For example, 0.1% lift can be significant with millions of users, but may not be worth implementing.

Solution: Always consider both p-value (statistical significance) and lift (practical significance). The calculator shows both—use it to reinforce the distinction.

3. Wrong p-Value Calculation for One-Sided vs Two-Sided Tests

Mistake: Using two-sided p-value formula for one-sided tests, or vice versa, leading to wrong p-values.

Why it's wrong: Two-sided: p = 2 × (1 - Φ(|z|)), one-sided: p = 1 - Φ(z) if z ≥ 0. Using wrong formula gives wrong p-values. For example, z = 1.96, using two-sided formula gives p = 0.05, but one-sided gives p = 0.025.

Solution: Always use correct formula based on test type: two-sided doubles the tail probability, one-sided uses single tail. The calculator does this correctly—observe it to reinforce p-value calculation.

4. Not Accounting for Sample Size in Interpretation

Mistake: Ignoring sample size when interpreting results, leading to overconfidence in small-sample results.

Why it's wrong: Small samples have high variability and may fail to detect real effects (false negatives) or give unreliable estimates. Large samples can detect tiny effects that may not be practically important. For example, small-sample significant results may not replicate.

Solution: Always consider sample size: small samples = less reliable, large samples = more reliable but may detect tiny effects. The calculator shows sample sizes—use it to reinforce sample size importance.

5. Peeking at Results and Stopping Early

Mistake: Checking results repeatedly and stopping when significance is reached, inflating false positive rates.

Why it's wrong: Multiple looks at data increase the chance of false positives. Stopping early when p < 0.05 is reached inflates the actual false positive rate above 5%. For example, checking 10 times increases false positive rate significantly.

Solution: Always pre-specify sample size and avoid peeking. If you must check early, use sequential testing methods. The calculator doesn't account for peeking—use it to reinforce the importance of pre-specification.

6. Confusing Lift Calculation

Mistake: Using absolute difference instead of relative lift, or calculating lift incorrectly.

Why it's wrong: Lift = ((Variant/Baseline) - 1) × 100, not (Variant - Baseline) × 100. Using absolute difference gives wrong lift. For example, baseline = 10%, variant = 12%, using (12-10) × 100 = 200% (wrong, should be 20%).

Solution: Always use: Lift = ((Variant/Baseline) - 1) × 100. The calculator does this correctly—observe it to reinforce lift calculation.

7. Not Realizing That This Tool Doesn't Account for Multiple Comparisons

Mistake: Assuming the calculator accounts for multiple comparisons, sequential testing, or peeking when it doesn't.

Why it's wrong: This tool performs a single test and doesn't adjust for multiple comparisons, sequential looks, or early stopping. Testing many metrics or variants without correction increases false positive rates. For example, testing 20 metrics at α = 0.05 gives ~64% chance of at least one false positive.

Solution: Always remember: this tool performs a single test only. You must use multiple comparison corrections (e.g., Bonferroni) for multiple tests. The calculator emphasizes this limitation—use it to reinforce that single tests and multiple tests require different approaches.

Advanced Tips for Mastering A/B Test Significance

Once you've mastered basics, these advanced strategies deepen understanding and prepare you for complex A/B testing problems:

1. Understand Why Pooled vs Unpooled Standard Errors Are Used (Conceptual Insight)

Conceptual insight: Pooled SE assumes null hypothesis (no difference) and is used for test statistic. Unpooled SE estimates actual difference and is used for confidence intervals. Understanding this provides deep insight beyond memorization: SE choice depends on whether you're testing a hypothesis or estimating a parameter.

2. Recognize Patterns: Larger Sample Size = More Power

Quantitative insight: Larger sample sizes reduce standard errors, making it easier to detect real effects (higher power). However, very large samples can detect tiny effects that may not be practically important. Understanding this pattern helps you predict power: larger n = easier to detect effects.

3. Master the Systematic Approach: Data → Rates → SE → z → p → Significance

Practical framework: Always follow this order: (1) Calculate conversion rates or means, (2) Calculate pooled proportion (for proportion tests), (3) Calculate standard error, (4) Calculate test statistic (z-score), (5) Calculate p-value, (6) Compare p-value to alpha, (7) Determine significance and winner. This systematic approach prevents mistakes and ensures you don't skip steps. Understanding this framework builds intuition about A/B testing.

4. Connect A/B Testing to Data-Driven Decision Making Applications

Unifying concept: A/B testing is fundamental to digital marketing (optimizing campaigns), product development (testing features), e-commerce (improving conversion), and business intelligence (data-driven decisions). Understanding A/B testing helps you see why it enables evidence-based decisions, reduces guesswork, quantifies improvements, and supports optimization. This connection provides context beyond calculations: A/B testing is essential for modern data-driven decision making.

5. Use Mental Approximations for Quick Estimates

Exam technique: For quick estimates: z ≈ 2 gives p ≈ 0.05 (two-sided), z ≈ 1.96 gives p ≈ 0.05 (two-sided). Lift ≈ (difference / baseline) × 100. These mental shortcuts help you quickly estimate on multiple-choice exams and check calculator results.

6. Understand Limitations: This Tool Assumes Simple Random Sampling

Advanced consideration: This calculator assumes: (a) Independent random samples, (b) Approximate normality of test statistic, (c) No peeking or sequential testing, (d) Single comparison (no multiple testing), (e) Fixed sample sizes. Real systems may show these effects. Understanding these limitations shows why proper experimental design and statistical methods are often needed, and why advanced methods are required for accurate work in research, especially for complex experiments or non-standard conditions.

7. Appreciate the Relationship Between Significance and Business Impact

Advanced consideration: Statistical significance affects business decisions: (a) Significant results = likely real effect, (b) Large lift = potentially high impact, (c) Confidence intervals = uncertainty about effect size, (d) Replication = validation of results. Understanding this helps you design experiments that use significance effectively and achieve optimal business outcomes.

Limitations & Assumptions

• Fixed Sample Size Assumption: This calculator assumes you've collected all data before analysis (fixed-horizon testing). Repeatedly checking results as data accumulates ("peeking") inflates false positive rates. For continuous monitoring, use sequential testing methods instead.

• Independent Random Sampling: Statistical formulas assume each user is independently sampled once. Violations occur with returning users, network effects, or shared accounts. Real experiments may need cluster-randomized designs or CUPED variance reduction techniques.

• Single Comparison Only: Results apply to one comparison. Testing multiple variants, metrics, or segments without correction dramatically increases false positive rates (multiple testing problem). Use Bonferroni, FDR, or hierarchical testing for multiple comparisons.

• Normal Approximation for Proportions: The z-test assumes sample proportions are approximately normal, which requires sufficiently large samples (typically np > 5 and n(1-p) > 5). For small samples or extreme proportions, exact tests may be more appropriate.

Important Note: This calculator is strictly for educational and informational purposes only. It demonstrates A/B testing concepts for learning. For production experiments, use specialized experimentation platforms (Optimizely, VWO, Google Optimize, internal tools) with proper sequential testing, multiple comparison corrections, and statistical rigor. Consult with data scientists for business-critical experiments.

Sources & References

The A/B testing statistical methods used in this calculator are based on established experimental design and hypothesis testing principles from authoritative sources:

Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. — Comprehensive guide to A/B testing methodology from industry experts.
Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. — Standard textbook on experimental design and statistical analysis.
Google Developers — A/B Testing Best Practices — Industry guidelines for implementing and analyzing experiments.
Optimizely Knowledge Base — optimizely.com — Practical resources for A/B testing concepts and statistical significance.

Note: This calculator is designed for educational purposes to help students understand A/B testing concepts. For production experiments, use specialized experimentation platforms with proper sequential testing controls.

Frequently Asked Questions

Does a significant result guarantee my variant will always perform better?

No. Statistical significance means the observed difference is unlikely due to chance under the test assumptions, but it doesn't guarantee future performance. Results can vary due to seasonality, user behavior changes, sample composition, and other factors. Always consider replication and practical significance alongside statistical significance. Understanding this helps you see why significance doesn't guarantee future outcomes and why replication is important.

What if I change my sample size mid-experiment?

Changing sample size during an experiment (especially based on interim results) can invalidate standard statistical tests and inflate false positive rates. This is called 'peeking' or 'optional stopping.' For proper mid-experiment adjustments, consider sequential testing methods designed for this purpose. Understanding this helps you recognize when sample size changes are problematic and why pre-specification is important.

Can I use this tool for medical or clinical trials?

No. This tool is for educational and exploratory purposes only. Clinical trials require rigorous protocols, regulatory approval (FDA, IRB), specialized statistical methods, and expert oversight. Never use this calculator for medical decision-making or clinical research. Understanding this limitation helps you use the tool for learning while recognizing that medical applications require validated procedures and regulatory compliance.

Is this tool sufficient for regulatory or compliance decisions?

No. Regulatory and compliance decisions require validated software, documented methodologies, audit trails, and expert review. This educational tool does not meet those standards. Consult qualified professionals and use appropriate validated tools for such decisions. Understanding this limitation helps you use the tool for learning while recognizing that regulatory work requires validated procedures and compliance.

What's the difference between statistical and practical significance?

Statistical significance tells you whether an effect is likely real (not due to chance). Practical significance tells you whether the effect matters in the real world. A 0.1% improvement might be statistically significant with enough data, but may not be worth implementing. Always consider both the p-value AND the effect size (lift) when making decisions. Understanding this helps you see why both statistical and practical significance matter and how to interpret test results correctly.

Why might my results show 'Inconclusive'?

Results are inconclusive when the observed difference isn't statistically significant at your chosen alpha level. This could mean: (1) there's truly no difference, (2) the sample size is too small to detect a real difference, or (3) the effect is too small to detect with current data. Consider running a power analysis to determine if you need more data. Understanding this helps you recognize when results are inconclusive and how to interpret them.

Should I use a one-sided or two-sided test?

Use a two-sided test when you want to detect any difference (positive or negative)—this is the more conservative choice and is generally recommended. Use a one-sided test only when you have a strong prior belief that the variant can only be better (not worse) than the baseline, and you're not interested in detecting negative effects. Understanding this helps you choose the appropriate test type and see why two-sided tests are generally preferred.

How do I interpret confidence intervals?

A confidence interval (e.g., 95%) gives a range of plausible values for the true difference between groups. If the interval doesn't contain zero, the result is statistically significant. The width of the interval reflects uncertainty—wider intervals indicate more uncertainty about the true effect size. Understanding this helps you see how confidence intervals convey uncertainty and why they're important for interpreting results.

What is the difference between proportion and mean tests?

Proportion tests compare binary outcomes (conversion or not) between groups, using conversion rates. Mean tests compare continuous numeric outcomes (e.g., revenue, time) between groups, using averages. Proportion tests use pooled standard errors for test statistics, while mean tests use Welch-style standard errors. Understanding this helps you see when to use each test type and why they use different calculations.

Does this tool account for multiple comparisons or peeking?

No. This tool performs a single statistical test and doesn't account for multiple comparisons, sequential testing, or peeking. If you test many metrics or variants, or check results repeatedly, you need to adjust for multiple comparisons (e.g., Bonferroni correction) or use sequential testing methods. Understanding this limitation helps you use the tool correctly and recognize when additional statistical methods are needed.

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