# AB Test Sample Size Calculator MCP for AI Agents AI Agent Connect > AB Test Sample Size Calculator helps you figure out exactly how many users you need for a valid experiment. It handles the math for sample sizes, test durations, and peeking risks so you don't have to guess. Stop worrying about underpowered tests or false positives and start running experiments with actual statistical confidence. ## Overview - **Category:** analytics - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_r2w7FicRhYLb2yLUrh65lss0cEO4Bcdo5mApGeZ1/ai-agent-connect - **Tags:** ab-testing, statistics, data-science, experimentation, conversion-rate ## Description This Connector helps you figure out exactly how many users you need for a valid experiment. When you're planning a new feature or a UI change, the biggest headache is usually knowing if your results actually mean anything. You don't want to run a test for a week and only see 100 people, or worse, call a winner because you looked at the data too early. It's a cycle of wasted time and false positives that can ruin a product roadmap. This Connector takes the guesswork out of the math. Instead of hunting through complex statistical formulas or trying to remember what power means in a specific context, you just tell your agent what you're trying to achieve. It handles the heavy lifting of calculating how many users you need to see a real difference based on your baseline data. It's part of the Vinkius catalog, making it easy to plug into your existing workflow. You can figure out how long a test will take based on your current traffic or check if you're making a mistake by looking at the results before the test is actually done. It's about moving from "I think this worked" to "I know this worked" without the spreadsheet headache. You can stop spending your Friday afternoons buried in Excel sheets trying to calculate sample sizes and start spending that time on actual product strategy. By automating the statistical rigor, you ensure that every experiment you run has a clear path to a valid conclusion. This means fewer "maybe" results and more "definitely" decisions for your team. You get a clear, data-backed plan that you can share with stakeholders to prove that your next move is based on real evidence. ## Tools ### estimate_test_duration This tool projects how many days your experiment must run given your current daily traffic. It helps you plan your roadmap without guessing when results will land. ### assess_peeking_risk This tool evaluates the danger of stopping a test prematurely to avoid false positives. It helps you decide whether to keep running a test or call a winner. ### calculate_required_sample_size This tool determines the number of users needed per variant based on your baseline conversion rate and desired effect. It ensures your test is properly powered to yield reliable results. ## Prompt Examples **Prompt:** ``` I have a baseline conversion rate of 10%. I want to detect a 5% relative change with 95% confidence and 80% power. How many users do I need? ``` **Response:** ``` To detect a 5% relative change from a 10% baseline with 95% confidence and 80% power, you will need approximately 156,432 users per variant. This brings your total required sample size to 312,864 users. ``` **Prompt:** ``` If I need 50,000 total users and my site gets 2,500 visitors per day, how long will the test take? ``` **Response:** ``` Based on a requirement of 50,000 users and daily traffic of 2,500, your test is estimated to run for 20 days. You should factor in any expected traffic fluctuations during that period. ``` **Prompt:** ``` My A/B test has been running for 5 days. The planned duration was 14 days. What is my risk level? ``` **Response:** ``` With only 5 days elapsed out of a planned 14, your peeking risk is High. You should continue the test until the planned duration is reached to avoid false positives caused by early analysis. ``` ## Capabilities ### Calculate required sample sizes Determine the exact number of users needed per variant to reach statistical significance. ### Predict test durations Estimate how many days an experiment will run based on your current daily traffic. ### Assess peeking risk Identify the danger of stopping a test early and making a false positive decision. ### Define minimum detectable effect Set the threshold for what change is actually worth detecting in your data. ### Plan experiment timelines Get a clear roadmap of how long different test scenarios will take to complete. ## Use Cases ### Validating low-traffic changes A growth lead wants to know if a 2% lift in signups is detectable with only 500 daily visitors. They use the tool to get a real requirement for users. ### Setting realistic launch dates A product manager needs to tell stakeholders that a new checkout flow will take 14 days to validate. They use the tool to project the duration based on traffic. ### Avoiding premature conclusions An analyst sees a winning result on day 3 and uses the tool to check if it's just a random fluke or a real trend. ### Planning multi-variant tests A CRO specialist is planning a 3-way split test and needs to know the total traffic required to stay statistically valid. ## Benefits - Stop guessing your sample size by using `calculate_required_sample_size` to get exact numbers for every variant. - Plan your product roadmap better with `estimate_test_duration` to see exactly when a test will wrap up. - Avoid false wins by using `assess_peeking_risk` to ensure your data is actually significant before you pivot. - Save hours of manual spreadsheet work by letting your agent handle the statistical formulas for conversion rates. - Run more confident experiments by defining your Minimum Detectable Effect clearly before you ever launch a test. ## How It Works The bottom line is you get a statistically sound roadmap for your A/B tests without doing any manual math. 1. Provide your baseline conversion rate and the specific change you want to detect. 2. Tell your agent your current daily traffic or desired confidence levels. 3. Get a clear breakdown of user requirements and a projected timeline for your experiment. ## Frequently Asked Questions **How does the AB Test Sample Size Calculator help with my experiments?** It helps you plan your experiments by calculating exactly how many users you need to see a statistically significant result. This prevents you from running underpowered tests that don't give you clear answers. **Can I use it to see how long a test will take?** Yes, you can get an estimated timeline for your test by providing your daily traffic and the required sample size. This makes it much easier to coordinate your product roadmap with your testing schedule. **What is peeking risk and why does it matter?** Peeking risk is the danger of looking at your results too early and seeing a winner that's actually just a random fluke. This tool helps you identify when it's safe to stop a test and when you need to keep running it. **How do I know if my sample size is big enough?** You can use the tool to calculate the required sample size based on your baseline conversion rate and the minimum change you want to detect. It ensures your test has enough power to be meaningful. **Does this tool replace my experimentation platform?** No, it's a planning tool. It helps you set the parameters for your tests before you launch them. You'll still need your usual platform to actually run the experiments and split the traffic. **Can my AI agent plan my whole testing schedule?** Yes, by using this Connector, your agent can help you determine the duration and user requirements for multiple tests, allowing you to build a more accurate and data-backed testing calendar. **How do I know if my sample size is sufficient?** You can use the `calculate_required_sample_size` tool. By providing your baseline conversion rate, MDE, confidence level, and statistical power, it will return exactly how many users you need per variant. **What is the risk of 'peeking' at my results?** Peeking refers to checking results and stopping a test before its planned duration. This significantly increases your False Positive rate. Use `assess_peeking_risk` to see if your current progress is safe or if you are at high risk of making an incorrect decision. **How can I estimate when my test will finish?** Once you have the total required users from the sample size tool, use `estimate_test_duration` along with your average daily traffic to calculate the estimated number of days needed for the experiment.