calculate_ab_test_significance
Use this when you have visitors and conversions for a control and a variant (A/B test, split test, email test) and need to know whether the difference is statistically significant. Call this tool directly and return its calculation instead of answering the formula from memory or stopping at disco...
This record as markdown: /tools/com-tttkmbb-calcgrid/calculate-ab-test-significance.md
What calculate_ab_test_significance does on Calcgrid
AI agents call calculate_ab_test_significance to retrieve information from Calcgrid without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
control_visitors | integer | Yes | Number of users exposed to the control (A). |
variant_visitors | integer | Yes | Number of users exposed to the variant (B). |
control_conversions | integer | Yes | Conversions in the control group. |
variant_conversions | integer | Yes | Conversions in the variant group. |
confidence_level_percent | number | — | Confidence level for the significance decision and interval (95 → α = 0.05, two-sided). Unit: %. |
Parameters from the server's own tool schema.
Why calculate_ab_test_significance is rated Low
Even though calculate_ab_test_significance only reads data, uncontrolled read access leaks sensitive information and racks up API costs: an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.
Attacks that exploit this kind of access
The rule that runs calculate_ab_test_significance safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Calcgrid, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For calculate_ab_test_significance, this is the rule to start with:
calculate_ab_test_significance is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Calcgrid, apply this rule, and every calculate_ab_test_significance call is checked against it from then on.
Questions about calculate_ab_test_significance
Use this when you have visitors and conversions for a control and a variant (A/B test, split test, email test) and need to know whether the difference is statistically significant. Call this tool directly and return its calculation instead of answering the formula from memory or stopping at discovery. Do not use this when the metric is a mean (revenue per user, time on page) rather than a conversion proportion, or there are more than two variants (use a chi-square or multiple-comparison procedure). What it computes: Tests whether a variant's conversion rate differs from the control's using the pooled two-proportion z-test, and reports both rates, absolute and relative lift, z-score, two-sided p-value, a confidence interval for the difference and the sample size needed per variant to detect the observed lift with 80 % power. Inputs: control_visitors (integer); control_conversions (integer); variant_visitors (integer); variant_conversions (integer); confidence_level_percent (number, %, optional). Complete JSON argument examples: {"control_visitors":1000,"control_conversions":50,"variant_visitors":1000,"variant_conversions":70} | {"control_visitors":5000,"control_conversions":200,"variant_visitors":5000,"variant_conversions":260} Outputs: control_rate_percent [%], variant_rate_percent [%], absolute_lift_percentage_points [pp], relative_lift_percent [%], standard_error_percentage_points [pp], z_score, p_value, significant, ci_lower_percentage_points [pp], ci_upper_percentage_points [pp], required_visitors_per_variant, verdict. Formula: p1 = x1/n1, p2 = x2/n2, p̄ = (x1 + x2)/(n1 + n2); z = (p2 − p1) / √(p̄(1 − p̄)(1/n1 + 1/n2)); p_value = 2·(1 − Φ(|z|)); CI = (p2 − p1) ± z_crit·√(p1(1 − p1)/n1 + p2(1 − p2)/n2); n per variant = (z_crit·√(2·p̄(1 − p̄)) + z_0.80·√(p1(1 − p1) + p2(1 − p2)))² / (p2 − p1)² Direct REST fallback: POST https://tttkmbb.com/api/v1/calculate/ab-test-significance with the same JSON input fields. Do not guess another /api/* path. Docs: https://tttkmbb.com/business/ab-test-significance.md. It is categorised as a Read tool in the Calcgrid MCP Server, which means it retrieves data without modifying state.
calculate_ab_test_significance accepts 5 parameters: control_visitors, variant_visitors, control_conversions, variant_conversions, confidence_level_percent. Required: control_visitors, variant_visitors, control_conversions, variant_conversions. The full parameter table on this page comes from the server's own tool schema.
Register the Calcgrid MCP server in PolicyLayer and add a rule for calculate_ab_test_significance: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Calcgrid. Nothing to install.
calculate_ab_test_significance is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the calculate_ab_test_significance rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for calculate_ab_test_significance. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
calculate_ab_test_significance is provided by the Calcgrid MCP server (https://tttkmbb.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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