# Test Duration Calculator MCP for AI Agents AI Agent Connect > Test Duration Calculator helps you figure out exactly how long an A/B test needs to run. It calculates required sample sizes, projects end dates based on your traffic, and flags risks like seasonality or long-running bias. It's built for teams that need to move from 'we think this works' to 'we know this works' without wasting weeks on underpowered experiments. ## Overview - **Category:** statistics - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_O1AMvmlC1UbiIg5EToBCmgYLv4CFNyrsTcP8VO1g/ai-agent-connect - **Tags:** ab-testing, statistics, experimentation, data-science, analytics ## Description The Test Duration Calculator is a specialized tool for researchers to determine the necessary length of A/B experiments. Running a test is a numbers game where guessing leads to bad data and wasted time. If you don't have the right sample size, your results are meaningless; if you run it too long, you're just collecting noise. This Connector handles the heavy lifting of experimentation math so you can focus on the actual product strategy. You can plug in your baseline conversion rates and the specific lift you're looking for to get a hard number on how many users you need to see. From there, it translates those numbers into a real-world calendar that accounts for things like seasonality and weekly cycles. It even looks ahead to tell you if an experiment is going to drag on so long that it might start skewing your data or introducing external bias. It's a handy part of the Vinkius catalog that lets you connect your preferred AI client and start working with a wide range of tools immediately. Many teams struggle with the 'when' of experimentation. You might have a great idea, but if the test takes three months to reach significance, the market might have moved on by the time you have your answer. This tool helps you identify those bottlenecks early. It ensures that every experiment you run is sized correctly for the impact you want to see. By getting these numbers right at the start, you avoid the frustration of ending a test only to realize the results are statistically insignificant. It's about making sure your data-driven decisions are actually based on solid math. ## Tools ### calculate_sample_size Determine the exact number of users needed per variant to reach your desired statistical power. This helps you avoid underpowered tests. ### estimate_timeline Convert sample size requirements into a calendar view that accounts for daily traffic and weekly cycles. It helps you plan your launch dates. ### get_finalization_details Project your final experiment end date and get a warning if the test duration poses a high risk of bias. Use this to avoid 'zombie' experiments. ## Prompt Examples **Prompt:** ``` I have a baseline of 3% conversion and I want to see a 10% lift. How many users do I need for 2 variants? ``` **Response:** ``` To detect a 10% relative lift with a 3% baseline across 2 variants, you'll need about **35,000 samples per variant**. That's a total of **70,000 users**. ``` **Prompt:** ``` If I get 5,000 users a day, how long will it take to get 50,000 samples per variant? ``` **Response:** ``` At 5,000 users per day, you'll hit your target in **10 days**. However, to avoid weekend bias and ensure a full cycle, I recommend running the test for **2 weeks**. ``` **Prompt:** ``` My test started on July 1st. If it takes 4 weeks, what's the end date and is it risky? ``` **Response:** ``` The test is projected to end on **July 29th**. Since it's hitting that 4-week mark, I've flagged it as a **high-risk duration** due to potential external interference. ``` ## Capabilities ### Calculate required sample sizes Determine the exact number of users needed per variant to reach your desired statistical power. ### Project experiment end dates Convert sample size requirements into a calendar view based on your daily traffic. ### Identify experiment risks Get warnings for long-running tests that might be skewed by external factors. ### Account for seasonality Ensure your test timeline captures full weekly cycles to avoid day-of-the-week bias. ### Determine user requirements Get hard numbers for required users based on your specific Minimum Detectable Effect. ## Use Cases ### Verifying detectable lift A growth lead needs to know if a 2% lift is detectable. They ask the agent to calculate the sample size for 3 variants. ### Roadmap planning A product manager wants to know if a test will take a month. They provide daily traffic to get a projected end date. ### Risk assessment An analyst is worried about a long-running test. They use the tool to check for risk levels and potential external interference. ### Holiday launch planning A marketing team needs to plan a holiday launch. They use the timeline tool to see how many weeks of data they'll actually collect. ## Benefits - Stop guessing on sample sizes. Use `calculate_sample_size` to get exact numbers based on your specific conversion goals. - See your launch dates early. `estimate_timeline` turns raw numbers into a calendar view so you can plan your roadmap better. - Avoid zombie experiments. `get_finalization_details` flags tests that run too long and might skew your results. - Account for seasonality. The timeline logic handles weekly cycles so your data isn't biased by day-of-the-week trends. - Reduce manual math errors. Let your agent do the heavy lifting instead of messing with complex statistical formulas in a spreadsheet. ## How It Works The bottom line is you get a concrete, data-backed timeline for your A/B tests instead of just guessing. 1. Input your baseline conversion rate and the lift you want to detect. 2. Provide your current daily traffic or user volume. 3. Get a projected end date and a risk assessment for your test. ## Frequently Asked Questions **How does the Test Duration Calculator help with A/B testing?** It tells you exactly how many users you need to see to trust your results. It removes the guesswork from your experiment setup so you don't waste time on tests that won't reach significance. **Can the Test Duration Calculator project my experiment end date?** Yes, it takes your daily traffic and required sample size to give you a specific calendar date. This helps you plan your product roadmap with actual dates instead of vague estimates. **Does the Test Duration Calculator account for weekly trends?** It does. The timeline tool is designed to suggest full weekly cycles to make sure your data isn't skewed by people behaving differently on weekends. **How do I know if my experiment is too long?** You can use the tool to get a risk assessment. It will flag experiments that run long enough to potentially pick up noise from outside your actual test. **Can I use the Test Duration Calculator for multiple variants?** Yes, you can specify how many variants you're testing. The tool will adjust the total sample size needed to ensure each variant has enough data. **What is the difference between sample size and duration?** Sample size is the total number of users you need to see. Duration is how long that will actually take based on your current traffic. This Connector calculates both. **How does the tool handle day-of-week bias?** The tool uses `estimate_timeline` to round up the required duration to the nearest full 7-day cycle, ensuring that weekly seasonality does not bias your results. **What is considered a high-risk experiment duration?** Any experiment projected to run longer than 4 weeks is flagged as high risk by `get_finalization_details` due to potential external noise like holidays or marketing changes. **Can I calculate sample size for multiple variants?** Yes, by providing the total number of experimental groups to `calculate_sample_size`, the tool will determine the cumulative required samples.