What is an experimentation platform? Features and how to choose one
Showing 2 versions of a feature is the easy part. Proving which version helped users requires a chain of decisions and data that you can inspect.
Someone has to define the hypothesis, assign users, record which experience they received, connect those records to outcomes, check data quality, and interpret uncertainty. If each step lives in a different script or spreadsheet, an A/B test becomes a coordination project before it becomes a product decision.
An experimentation platform is the shared infrastructure and workflow for designing, running, analyzing, and documenting controlled experiments. In product development, that usually means comparing randomly assigned experiences to estimate how a change affects user behavior or business outcomes.
A useful platform makes the measurement process easier to repeat and harder to misinterpret. It does not supply a good hypothesis, repair missing events by itself, or guarantee that a new feature will win. Choose one by testing how it handles your application's identity, data, release controls, and decision process together.
What an experimentation platform does
It connects a change to an interpretable outcome
Consider a hypothetical onboarding experiment. A team believes that replacing an empty project screen with a guided first task will increase successful project creation without raising errors or support demand.
The platform helps the team define the eligible population, assign accounts to control or treatment, deliver the correct experience, record exposure, and calculate the chosen metrics. The analysis then estimates the effect and its uncertainty. The team records whether to ship, revise, continue measuring, or stop.
These stages have to agree. If assignment uses an account ID but the outcome table only contains anonymous browser IDs, the join may lose or duplicate observations. If the application records exposure before anyone can see the relevant screen, the effect may be diluted. If the metric's observation window ends too soon, the analysis answers a different question.
The architecture of an experimentation system spans execution, data processing, and analysis, as well as the interface where people configure the test. An attractive result screen is only one part of the system you are evaluating.
It differs from adjacent tools
Product analytics describes behavior: where users drop out, which events occur together, or how a metric changes over time. An experiment adds a controlled comparison designed to estimate the effect of an intervention. A before-and-after chart is not automatically a randomized experiment.
Feature flags control exposure. They can turn a capability on for a cohort, support gradual rollout, or disable a broken feature. Experimentation connects that control to assignment, measurement, and inference. A release can be operationally healthy without improving the intended product outcome.
Offline model evaluation measures performance against a dataset, rubric, or simulated workload. Online experimentation measures outcomes under real product exposure. For an AI feature, both can be useful: offline checks can identify quality problems before release, while an online test can measure whether users complete tasks more successfully. Neither replaces the other.
The scope also varies between platforms. Some integrate delivery, data capture, analysis, and workflow. Others analyze experiments whose assignments and events are produced elsewhere. Evaluate the complete path you need, including components outside the platform's dashboard.
Features that make experiment results usable
Consistent identity, assignment, and exposure
Start with the randomization unit: user, account, device, session, or another entity. The right choice depends on how the product is used and where treatment can affect other participants. A collaboration feature assigned independently to teammates can create spillovers between control and treatment.
Ask how the platform maintains assignment when a user signs in, changes device, or returns after a deployment. Inspect the identifier, experiment key, allocation rules, and any conditions that can change the experience. Experiment design must account for stable identities and interference; deterministic hashing alone does not solve an incorrect choice of unit.
Then inspect exposure logging. An assignment can exist without a user encountering the changed feature. The event should represent the decision boundary needed for your analysis and include an experiment identifier, variation, unit identifier, and timestamp. Check how duplicate events and multiple exposures are handled.
A standardized evaluation interface can help application integration, but it is not a promise that 2 systems make identical assignments. Evaluation context and targeting keys describe inputs to flag evaluation. Matching those inputs does not automatically match hashing algorithms, experiment configuration, or exposure semantics across platforms.
Shared metrics with inspectable definitions
A metric needs more than a friendly name. Define the eligible population, unit of analysis, numerator, denominator, event filters, observation window, and treatment of missing or extreme values. “Activation” should not mean one thing in the experiment and another in the weekly business report.
Look for a shared metric catalog, ownership, review, and a way to inspect the calculation. Confirm that the platform supports the types your use case requires, such as binary conversion, revenue per assigned account, ratios, retention, or latency percentiles. A workaround that flattens every outcome into a conversion rate can change the meaning of the decision.
Sensitivity and interpretability are separate properties of a good experiment metric. A measure should detect relevant changes while still representing something useful. More events or a faster dashboard refresh cannot compensate for measuring the wrong behavior.
In GrowthBook, Fact Tables and metric definitions connect calculations to warehouse data, including explicit metric windows. The evaluation question is whether your team can reproduce those definitions and keep them consistent as the product evolves.
Transparent statistics and data quality checks
Ask which statistical methods are supported, what their outputs mean, and which assumptions your experiment must satisfy. You should be able to explain the effect estimate, uncertainty interval, decision threshold, and relevant limitations to someone who has not used the dashboard.
Bayesian and frequentist approaches answer related questions through different frameworks. Sequential methods address repeated observation and stopping. Multiple-testing procedures address families of comparisons. These are not interchangeable labels for “more accurate results.” Evaluate the exact method and your team's ability to use it correctly.
For example, continuous monitoring requires an appropriate inference procedure if you plan to stop based on the emerging result. A platform that refreshes a fixed-horizon p-value every hour has not, by that fact alone, made optional stopping valid.
Trust checks matter before effect interpretation. Sample ratio mismatch can reveal problems across assignment, execution, processing, and analysis. Look for diagnostic detail and a response workflow, rather than only a warning icon. Also inspect event loss, identity join rates, duplicate data, and unexpected metric values.
Release controls and a durable decision record
The platform should make it clear which changes are drafts, which are active, who can publish them, and how exposure can be paused. For a feature-flag experiment, the final decision and the final flag state are related but separate tasks. Stopping analysis does not necessarily remove the experiment's application behavior.
Evaluate project permissions, review flows, change history, and recovery procedures in the same pilot as the statistical result. These controls reduce ambiguity when multiple teams share the platform. They should be understandable enough for the people who actually operate releases.
Finally, record the hypothesis, configuration, metric definitions, result, limitations, and action. An experiment that prevents a harmful release can be valuable even if it never produces a winning variation. The record should help the next team avoid repeating the same mistake.
Inspect the trust checks first
Use the lessons from trustworthy experiment design to assess the assignment, measurement, and interpretation behind a platform's result screen.
Read the Experiment Lessons
Choose an architecture that fits your application and data
Follow the data from exposure to outcome
An event-capture architecture can collect exposure and outcome events through a platform's ingestion pipeline. A warehouse-based architecture can analyze assignments and outcomes in an existing data store. Hybrid arrangements can combine these paths. The useful question is where each authoritative record lives and how the pieces are joined.
If revenue is reconciled in your warehouse, a test based only on a browser purchase event may diverge from the business definition. If your warehouse pipeline runs once a day, a fast query engine cannot make tomorrow's data available now. Inspect freshness, correction handling, identity mapping, and query costs.
With data source and assignment-query configuration, you can make the relationship between an experiment's exposure records and business data explicit. Whichever architecture you choose, verify a few individual records across the full path before trusting aggregate totals.
Warehouse-native does not mean that no information ever crosses a system boundary. Review what queries run, what results or metadata are retained, what credentials are used, and which services can access them. Match that data flow to your actual deployment requirements rather than relying on a category label.
Match delivery to the place the change runs
A marketing-page text test, a backend recommendation change, and a mobile onboarding experiment have different implementation requirements. Check the runtime you need, how variants are delivered, and what happens before configuration is available.
Browser-side changes need attention to rendering, flicker, content security policy, and exposure timing. Server-side changes need consistent request context and reliable exposure logging. Mobile applications need version compatibility and an update strategy that accounts for clients that stay offline or retain old application versions.
Use this comparison to prepare the pilot:
| Use case | Integration to prove | Common failure to investigate |
|---|---|---|
| Web content or funnel change | Variant rendering and browser exposure | Exposure fires before the changed element can be seen |
| Backend product behavior | Server assignment, evaluation, and outcome join | Request identity differs from the metric identity |
| Account-level collaboration | Shared assignment and team behavior | Members of one account receive conflicting experiences |
| Mobile feature | Supported app versions and configuration refresh | Old clients lack the expected flag or fallback |
The platform does not need to own every implementation layer. It does need a documented, testable contract with the layers that it does not own.
Decide which operational responsibilities to keep
A managed service can reduce infrastructure maintenance, while self-hosting can offer more control over the application's environment. Neither removes the need for instrumentation, metric ownership, or statistical expertise.
If self-hosting matters, assess upgrades, backups, authentication, monitoring, capacity, and incident response. If using a hosted service, assess integration dependencies, data access, exportability, and the operating model when a service is unavailable. Include both the experimentation application and any warehouse workloads in the responsibility map.
Ask how your team would migrate away. Can you export experiment configurations, metric definitions, assignment records, and durable findings? Which behavior is embedded in SDK calls? A clear exit path is part of understanding the architecture, even when you expect to stay.
Coordinate experiments that share product behavior
As more teams adopt the platform, ask how they discover other experiments affecting the same audience or code path. A checkout redesign and a pricing experiment may change a shared decision surface. Independent assignment does not automatically make their combined user experience sensible.
Look for ways to document related experiments, inspect overlap, and apply exclusion when the design requires it. Do not demand that every test be isolated from every other test. Interactions are not a universal reason to run experiments serially; the need depends on the interventions and the question you want to answer.
Include one realistic overlap in the pilot. Ask an engineer to identify the active configurations, explain which combinations users can receive, and demonstrate the intended coordination mechanism. The result should be a documented rule your teams can follow, with an owner who can resolve conflicts. This evaluates a practical scaling requirement without assuming that a long feature list is necessary for your first experiment.
Evaluate platforms with a production-shaped pilot
Start with one decision and explicit acceptance criteria
Choose a use case that represents the work you expect to repeat. A simple color test may be useful for initial wiring, but it will not establish whether account-level assignment and warehouse-defined revenue work for a B2B product.
Write the hypothesis, randomization unit, exposure point, primary metric, guardrails, minimum detectable effect, expected recruitment rate, and observation period. State what a pass means for implementation and measurement separately. A/B test design is part of platform evaluation, not something to postpone until after procurement.
Give engineering, data, and product owners concrete tasks. Engineering verifies assignment and delivery. Data verifies joins, definitions, and reproducibility. Product verifies that the result supports an actual decision. Security or infrastructure owners inspect the relevant access and deployment requirements.
Separate requirements that determine fit from conveniences you can add later. Account-level randomization may be essential for a team product. A particular chart layout probably is not. A required data region, supported mobile runtime, or approval boundary should be demonstrated before you spend time scoring minor interface differences.
For each essential requirement, ask what happens if it fails. If the platform needs an identifier you cannot reliably produce, that is an integration problem to resolve before launch. If a metric requires a warehouse table that does not exist, name the modeling work and its owner. An evaluation is stronger when it makes these dependencies explicit rather than hiding them inside an overall feature score.
Test normal operation and failure cases
Run an A/A test as part of the integration check, with the same experience in both groups. Inspect assignment balance, exposure logging, and metric plausibility. An A/A result can help reveal implementation problems; one nonsignificant result does not certify the entire system.
Also use controlled fixtures or a small test dataset where expected counts are known. A convenient aggregate can hide duplicate joins. A record-level reconciliation helps explain why the platform and an existing report differ.
For example, create a synthetic fixture with 2 exposed accounts in each variation. In control, one account makes purchases of $10 and $15 while the other makes none. In treatment, one account purchases $20 and the other makes none. Revenue per exposed account should be $12.50 in control and $10 in treatment. These tiny numbers test arithmetic and joins; they are not enough to estimate a product effect.
That fixture can expose several mistakes. Averaging order values answers a different question from revenue per exposed account. An inner join to orders drops the zero-purchase accounts. Duplicate assignment rows can multiply purchases if the analysis joins without the intended deduplication. Change one condition at a time and verify that the platform's result changes in the expected way.
Include a deliberately missing event, delayed outcome data, an evaluation failure, and an attempted change without the required permission. Test how to stop exposure and how to return to a baseline. You are evaluating how the workflow behaves when assumptions fail, not only whether a happy-path experiment launches.
| Pilot requirement | Evidence to collect | Owner |
|---|---|---|
| Assignment remains consistent | Repeated evaluations for representative identities | Engineering |
| Exposure joins correctly | Sample records and expected denominator reconciliation | Data |
| Metrics match business meaning | Definition, query, windows, and known-case totals | Data and product |
| Results are interpretable | Effect, uncertainty, stopping rule, and decision explanation | Experiment owner |
| Production changes are controlled | Draft, review, activation, and recovery exercise | Engineering and release owner |
| Findings remain usable | Saved result, caveats, and final feature disposition | Product |
Record failures and the effort to fix them. A platform can still be a good fit when setup needs work, provided the work is understood and the resulting process is repeatable.
Require an example of a useful inconclusive result as well. Ask the evaluator to explain what range of effects remains plausible, why the test did not resolve the decision, and which action the team can take. A tool that only makes statistically significant winners easy to present can encourage overconfident decisions when the evidence is ambiguous.
Price the repeated workflow, not only the subscription
Estimate the total effort for adding an experiment after the pilot. Include instrumentation, metric review, SDK maintenance, warehouse compute or event processing, permissions, interpretation, and cleanup. A low subscription price does not help if every test requires a bespoke data project.
Review the actual contract and included capabilities. Ask how seats, traffic, events, queried data, environments, support, and required governance features affect your cost. Avoid comparing headline prices while assuming different usage levels or editions.
Then estimate operational capacity. If analysis is the bottleneck, easier flag creation alone may not increase useful experiment throughput. If traffic is insufficient for the effects you care about, switching tools will not create statistical information. The pilot should expose those limits before the team commits to an ambitious testing target.
Where GrowthBook fits in the evaluation
Shared release control and warehouse-backed analysis
GrowthBook's experimentation platform connects feature delivery and experiment analysis with shared metrics and workflows. It supports multiple implementation paths and analysis against a team's data infrastructure. That makes it a relevant candidate when your main requirement is connecting product changes to existing business metrics.
Use the pilot to verify your identity and assignment data, metric definitions, and target runtimes. Review GrowthBook's statistical methods with the person responsible for analysis, including how the selected engine and advanced settings affect interpretation.
For release behavior, evaluate feature flag controls independently of the result screen. Test gradual exposure, the safe baseline, and the review and publication steps your team requires. Confirm current feature availability for the deployment and plan you intend to use.
Bring a real workflow to the discussion
GrowthBook will not define a reliable activation event or correct an inconsistent account ID automatically. Its value depends on the surrounding application and data contracts being explicit enough to reuse.
Start with a bounded use case and verify the experiment implementation before expanding it. If your evaluation involves several teams, an unusual identity model, or specific governance requirements, bring the pilot's acceptance criteria to a GrowthBook demo. The useful outcome is a clear implementation and validation plan, not a checklist of features you may never use.
Build a repeatable decision process
After the pilot, define what every experiment owner must provide and which parts the platform can standardize. Reuse a small set of reviewed metric definitions, analysis defaults, release checks, and result-record templates. Give people a way to escalate unusual designs rather than forcing every test into the same template.
Measure whether the process produces trustworthy decisions with reasonable effort. Track setup friction, instrumentation failures, time to a usable readout, and whether findings inform later work. Experiment count alone can reward activity without showing that the program helps users or avoids bad releases.
The right experimentation platform makes the complete chain visible: what changed, who received it, what happened, whether the evidence is trustworthy, and what the team decided. Prove that chain once with a real use case, then use it as the foundation for the next experiment.
Prove your first experiment path
Connect GrowthBook to a bounded product use case, verify assignment and metrics, and build the repeatable workflow your next test will use.
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Related Articles
CLI vs MCP: TLDR, it doesn’t matter as much as you think
Your agent should know how to do the job whichever interface it uses.
Your coding agent can already run terminal commands. Your product manager's assistant can connect to remote tools. Both need to inspect the same experiment, understand the same metric, and prepare changes to the same feature flag.
That creates a familiar question: should you give them a CLI or an MCP server?
The usual answers mix several concerns. A CLI is easy to compose with scripts. MCP gives compatible clients a standard way to discover and invoke tools. Skills can supply the instructions that make either route useful. None of those statements tells you whether the agent knows your release process or can distinguish a draft from a live change.
For GrowthBook, the choice has become smaller. We've built the agent layer around a REST API and reusable workflow skills, then made those available through different interfaces. Your choice of CLI or MCP no longer needs to determine which product workflow your agent can understand.
It still determines how the agent connects, executes requests, and handles credentials. This article explains that distinction, shows the same disabled-flag operation through both routes, and gives you a way to evaluate the integration you actually intend to use.
The debate combines three different questions
Start with a specific job: prepare a release control for a new checkout flow. The agent needs to know which project owns checkout, which environments exist, what the default behavior should be, and whether the proposed flag key already exists.
Choosing an interface answers only part of that job.
How can the agent reach the product?
A command-line interface lets an agent invoke a program in an environment where it has shell access. That program can make authenticated API requests and return data. You can also run the same commands yourself, put them in scripts, and preserve output for inspection.
The Model Context Protocol architecture connects a host application to servers through clients. An MCP server exposes capabilities that a compatible host can discover and call. The host might be a coding environment, a desktop assistant, or another agent application.
These categories overlap. A terminal-based agent can also be an MCP client. A local MCP server can be a process launched by that client. A remote MCP connection can work without the host giving the model a general-purpose shell.
So “terminal agent” doesn't automatically mean “use the CLI,” and “MCP” doesn't automatically mean “remote service.” Ask what access the runtime provides before choosing the adapter.
What procedure should the agent follow?
An API can expose a create operation without teaching an agent when to use it. A syntactically valid feature flag might still have the wrong project, an ambiguous key, or a default value that doesn't match the application.
That's the job of a skill: reusable instructions for a recognizable task. The Agent Skills format gives those instructions a portable structure, including a skill entry point and supporting references. A workflow can describe the context to inspect, the sequence to follow, and the evidence to return.
For the checkout example, useful guidance says to resolve environments, check the permanent key, choose a value type, and create the flag disabled. Merely exposing a create command leaves those decisions to the agent's general knowledge.
Skills are also useful when the right answer is to take no action yet. “Create a flag” and “design an experiment” are related requests, but they can require different objects and sequencing. An agent needs enough product guidance to make that distinction.
What context makes the procedure relevant?
Even a good playbook needs the current product state. The agent should inspect the objects in your GrowthBook organization and, when it is changing application code, the repository's SDK initialization and tests.
Those are different sources of context. GrowthBook supplies flags, experiments, metric definitions, and other authorized product resources. Your coding runtime supplies the application files it can access. An MCP connection doesn't by itself grant repository access, and a CLI doesn't by itself explain your metric definitions.
Evaluate the combination: access to the product, a maintained procedure, and enough context to apply that procedure. A comparison that changes all three at once cannot isolate the effect of CLI versus MCP.
GrowthBook puts the durable parts below the interface
We separated the agent layer into responsibilities that can be reused. The product API exposes operations. Skills explain workflows. The interface connects the agent to those two layers.
This is the architectural shift behind GrowthBook's agent experience. Instead of making every client integration carry its own interpretation of experimentation, we can share the interpretation and change how it is delivered.
The API provides the operations
GrowthBook's REST API makes product objects addressable. An agent can inspect a project, retrieve a flag, or perform an authorized operation against those objects.
The rebuilt API described in GrowthBook 4.4 uses shared schemas to support types, validation, and API documentation. The CLI is generated from the API definition. That reduces the amount of separately maintained command logic needed to expose product operations.
The OpenAPI specification explains the underlying principle: a machine-readable description can represent an HTTP API's paths, operations, inputs, and responses. Generation is useful because it derives another interface from that contract.
Generation doesn't mean any installed binary knows every future endpoint. The generated client still has a version, and your GrowthBook server still has a version. A self-hosted instance can lag behind the public documentation. Treat API compatibility as a deployment fact you verify.
Skills provide the workflow guidance
GrowthBook's feature-flags skill routes requests to more specific workflows. Creating a flag, inspecting dependencies, changing targeting, requesting review, and publishing a revision are separate procedures.
That structure lets the agent load the guidance relevant to the current job. The skill authoring guidance on progressive disclosure supports this approach: keep the entry point focused and place detail in references that are loaded as needed.
The benefit is maintainability as well as context management. A correction to a flag workflow can be made in the maintained skill rather than repeated across a series of tool descriptions and client-specific prompts.
There are two shell concepts to keep distinct. The generated growthbook CLI is a terminal interface to the product API. The skills repository also includes a small gb-call helper for making REST requests. Installing a skill doesn't make that helper a globally available command; its location depends on the installation. Both can reach the API, but their configuration and invocation are different.
MCP provides a thin bridge
The current GrowthBook MCP implementation exposes four tools: growthbook_list_skills, growthbook_read_skill, growthbook_api_read, and growthbook_api_write.
An agent discovers a domain, reads the relevant workflow, then makes API calls. It doesn't need a dedicated MCP tool for every product endpoint. Calls described as gb-call GET in a skill map to the read tool; mutating methods map to the write tool with the same path and body.
The bridge does not run the shell helper. It forwards authenticated requests. Product-side permissions and validation remain important because a small tool surface can still reach many operations.
Shared guidance also has a delivery boundary: the MCP server bundles skills when it is built, using a pinned source revision. A newly edited skill in the repository needs to be included in a subsequent server build before that server supplies it. “Shared source” is a maintainability claim, not a promise of instant synchronization.
Explore the shared flag workflows
Open the feature-flags skill to see the procedures that guide creation, targeting, reviews, and publishing. Bring the same workflow knowledge to the agent interface your team uses.
View the Flag Skill
The same disabled flag can use either route
The practical question is whether the interface changes the job. Let's create a Boolean flag called checkout-redesign, with a false default and every environment disabled.
This is an API example for a sandbox project, not an application integration tutorial. It creates a control object; your application still needs a correctly configured GrowthBook SDK to evaluate it. The feature flag documentation covers the product concepts behind that integration.
Connect and verify before preparing the write
For the terminal route, install the current GrowthBook CLI, configure a Personal Access Token through its interactive setup, and inspect the available command syntax:
Check that the active server and identity are the ones you intend to use. Prefer a tested, pinned CLI version in repeatable automation. Don't include a live token in a command you plan to share.
For an MCP route, the GrowthBook Cloud setup uses the hosted endpoint and browser OAuth. In a client using the documented mcpServers configuration shape, such as Cursor, add:
Complete the browser authorization and confirm tool discovery. Other clients can use a different configuration shape. Self-hosted GrowthBook users should follow the local stdio or self-hosted HTTP instructions rather than point a private instance at the Cloud service.
Next, let the connected agent discover the flag-creation workflow. These are tool-call parameters, shown separately in the order to invoke them:
The returned guidance should describe the prerequisites for creating the flag. If the workflow isn't present, resolve the server's bundled skill version before substituting instructions from an older integration.
Resolve real IDs and construct one payload
The flag-creation workflow checks existing keys, lists projects, and resolves environments. In the MCP client, invoke the read tool with each of these argument objects:
For the CLI route, perform that discovery with the corresponding command groups documented in the CLI command catalog:
Inspect each command's help if your installed version differs. The returned keys, project IDs, and environment IDs are the inputs to the payload below. In MCP, the equivalent read-tool arguments are:
Verify that checkout-redesign isn't taken, identify the sandbox project's ID, and enumerate every actual environment. These read responses determine the payload; don't treat example environment names as a complete inventory of your organization.
Save the following as flag.json, replacing <PROJECT_ID> and the environment keys with the discovered values. This example assumes the only environments are staging and production:
The API represents defaultValue as a string, even for a Boolean flag. Explicitly disabling each environment avoids relying on the organization's default state for newly created flags. Use a PAT for this example so the caller has a user identity for attribution.
Review the permanent key, project, value, and disabled states before submitting. If the intended job is an experiment, route to experiment design first instead of creating an unrelated flag and hoping to connect it later.
Submit once, then read the object back
With the CLI, the create command accepts a JSON body. In a Bash-compatible shell, inspect the request with dry run before submitting:
The first command previews a request; it doesn't prove the server will accept it. Run the second only after checking the preview and approving this creation. In PowerShell, use that shell's file-reading syntax rather than paste the Bash substitution unchanged.
The PowerShell equivalent, using the same reviewed file, is:
The same distinction applies: preview first, approve the specific creation, then submit. Neither command changes the application's deployed code.
Through MCP, call growthbook_api_write with the same payload serialized into its body string. Substitute the same resolved project and environment names:
Choose one creation route. Don't run both against the same key; they target the same product object, and the second creation should encounter an existing key.
Verify through the CLI get command:
Or invoke growthbook_api_read with:
Check the returned key, project, value type, default value, disabled state of every environment, and absence of unintended rules. Report those facts rather than “success.” No targeting or exposure change is included in this example.
Choose the route that fits the runtime
Once the procedure and API operation are shared, the remaining choice is operational. Where does the agent run? What can it execute? How does it authenticate? What does your team need to inspect afterward?
Shell access favors terminal composition
A shell-capable agent can run the CLI alongside repository tools, save structured output, and combine product inspection with local checks. That is useful when the job includes code changes, generated files, or a reproducible script.
For example, a flag audit can produce an inventory file while an engineer separately checks code references. The agent should preserve the association between the GrowthBook key and each repository reference. The final artifact can include both the product state and the proposed code diff.
The shell makes composition available; it doesn't make a workflow safe by default. A script can select the wrong profile or swallow an error. Decide which failures stop the workflow, where outputs are stored, and which steps need a reviewable artifact.
Remote tool access favors MCP clients
MCP is useful when the host already provides a tool connection but doesn't expose a terminal. A PM can inspect an experiment through a compatible assistant without installing a product CLI inside that assistant's runtime.
The MCP authorization specification defines an authorization framework for HTTP transports. GrowthBook Cloud uses browser OAuth, while local stdio configurations use credentials supplied to the process. Those are distinct deployment paths.
MCP also gives hosts a structured description of tools. The tool specification includes annotations such as read-only and destructive hints. Hosts can use those signals in their interaction design, but the signals are not an access-control boundary. The product must still enforce the caller's actual permissions.
Use a small decision table
| Your immediate requirement | Route to consider | What to verify |
|---|---|---|
| Combine product operations with local files and scripts | CLI plus maintained skills | Shell access, profile, installed version, structured output |
| Connect a compatible assistant without a shell | Hosted MCP | HTTP/OAuth support, identity, tools, bundled workflow |
| Keep requests inside a self-hosted deployment | CLI or self-hosted MCP | Network reachability, server version, credentials, trusted app origin |
| Reuse a procedure across different team clients | Shared skills through either route | Workflow revision, API compatibility, expected review artifact |
You can support more than one row. An engineer's CLI and a PM's MCP connection can operate on the same objects with different identities and permissions. Standardizing the workflow is often more useful than standardizing everyone's client.
Compare actual outcomes, not tool counts
Four MCP tools can address a large API. A large CLI command catalog can expose the same operations. Counting entries tells you little about whether an agent completed the intended job correctly.
Likewise, there is no universal token or speed result implied by the architecture. The client, model, instructions, response size, pagination, and number of requests all affect a real run. Measure the work you need instead of importing a result from an unrelated integration.
Keep the evaluation task constant
Use one sandbox project and one bounded task. For a read test, ask each route to find a named flag and identify its enabled environments, current revision, and relevant dependencies. For a write test, use separate temporary keys with the same reviewed disabled-flag requirements.
Give both runs the same expected output. Record whether each identified the correct objects, loaded the relevant workflow, handled missing data, and preserved the boundary between creation and exposure. Measure elapsed time and context consumption only alongside those correctness checks.
If one run gets rich workflow guidance and the other gets only a command name, you're testing the guidance as well as the interface. That can still be a useful product evaluation, but describe what changed.
Test failure behavior deliberately
Try a nonexistent object, an existing key, and a credential without the required permission. The agent should identify the failure and stop the dependent action. It should not invent an ID, silently pick another environment, or reinterpret a permission error as permission to retry with a broader credential.
The revision workflow adds another important test: multiple open drafts. When a chain of changes belongs in one revision, the agent must preserve its version rather than repeatedly ask for whichever draft is latest.
A creation receipt and a publication receipt are different artifacts. If your evaluation only checks that a request returned without error, it can miss the point where a proposed change became live or where a draft failed to include the intended edits.
Record the versions behind the result
Capture the CLI version, server version when available, client configuration, and the skill text or revision used. With MCP, inspect the guidance actually returned by the connected server. With installed skills, inspect the local instructions actually loaded.
That record makes a later regression diagnosable. A changed result may come from new guidance, a client update, a changed API, or different product state. “MCP became worse” is too broad to help you fix it.
Build workflows that survive the next client
The most useful consequence of GrowthBook's design is organizational. Engineers, PMs, and analysts can choose the agent surface that fits their work while referring to the same flags, experiments, and metrics.
An engineer can prepare a disabled flag from a terminal. A reviewer can inspect its draft in the app. An assistant connected through MCP can retrieve the same object and explain its state. The handoff should carry an ID, a revision where applicable, the proposed behavior, and the decision still required.
This is where the CLI-versus-MCP question loses much of its importance. Your release procedure doesn't need to be rewritten around each client. The product holds state; the API exposes operations; skills explain the work; the chosen interface delivers access.
Start with one inspection or sandbox creation. Require the agent to return a concrete object and its verified state. Then add the next workflow only when the current one is reliable enough for your team to review.
GrowthBook's feature flag platform gives that work a common place to live. The durable choice is how you connect release control and measurement to your team's process. CLI and MCP are ways to carry that process into the tools people already use.
Start with a common foundation
Set up GrowthBook for the flags, experiments, and metrics your agents will share. Then choose the CLI or MCP connection that fits each team member's work.
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Standard deviation vs variance: Key differences for A/B testers
A metric can have wide user-to-user variation and still yield a precise experiment estimate.
That is why confusing standard deviation with standard error creates bad A/B testing decisions. A dashboard may show that users differ substantially while the average effect is estimated closely enough to guide a release. Those statements can both be true.
Variance and standard deviation describe spread. Variance works on a squared scale; standard deviation takes its square root to return to the metric's original units. Neither is automatically the uncertainty around an experiment result.
For A/B testers, the useful distinction is practical: use standard deviation to communicate outcome variability, use variance in the calculations that combine uncertainty, and inspect the standard error or interval for the treatment effect. Start by making sure all three describe the correct unit of analysis.
Understand the difference in units
Variance uses squared distances
Outcomes above and below a mean have deviations with opposite signs. Simply averaging those signed deviations gives zero. Variance avoids that cancellation by squaring each deviation before combining them.
The sample variance formula is s² = Σ(xᵢ − x̄)² / (n − 1), where x̄ is the sample mean and n is the number of observations. The symbol Σ means to add the terms across observations.
If the outcome is measured in seconds, variance is measured in seconds squared. That is mathematically useful but less intuitive for explaining a product experience. “Variance is 4 seconds squared” does not mean users differ by 4 seconds.
Standard deviation returns to the original scale
Standard deviation is s = √s². A variance of 4 seconds squared corresponds to a standard deviation of 2 seconds. Both describe the same dispersion, expressed on different scales.
Do not define standard deviation as the average absolute distance from the mean. That is a different measure. Squaring deviations gives large deviations more influence, and taking the square root afterward does not undo that weighting.
| Quantity | What it describes | Units for task time |
|---|---|---|
| Variance | Squared dispersion in outcomes | Seconds squared |
| Standard deviation | Outcome dispersion on the original scale | Seconds |
| Standard error of the mean | Sampling variability of an estimated mean | Seconds |
Neither variance nor standard deviation requires a normally distributed population merely to be calculated. Normality matters for particular interpretations and inferential procedures, not for the existence of these summaries when the required moments are finite.
Calculate both from the same observations
Work through a small dataset
Use four synthetic task times: 2, 4, 4, and 6 seconds. This tiny dataset is for arithmetic, not evidence about a real product or a basis for confident inference.
The mean is (2 + 4 + 4 + 6) / 4 = 4 seconds. Deviations from the mean are −2, 0, 0, and 2 seconds. Their squares are 4, 0, 0, and 4 seconds squared, totaling 8.
Treating these observations as a sample, the usual sample variance is 8 / (4 − 1) = 8/3, or about 2.667 seconds squared. The sample standard deviation is √(8/3), or about 1.633 seconds.
This is a useful check on a report. If someone describes the same sample's variance as 1.633, they have probably substituted the standard deviation. If the units are missing, the mistake is much harder to spot.
Choose sample or population formulas deliberately
If those four observations constitute the entire finite population you want to describe, population variance divides by 4: 8/4 = 2 seconds squared. Population standard deviation is √2, or about 1.414 seconds.
The population and sample distinction depends on what you are estimating. Having every recorded event from an experiment does not necessarily make those events the full population of future users or future conditions you care about.
The n − 1 denominator makes the usual sample variance an unbiased estimator of population variance under independent, identically distributed sampling with finite variance. It does not make the square root exactly unbiased for population standard deviation. Nor does it correct biased sampling or dependence between observations.
Verify the calculation with explicit function names
Python's statistics functions distinguish sample calculations from population calculations. With Python 3 installed, this standalone example requires no external packages or data:
The first line of output is 2.667 1.633; the second is 2 1.414. The different denominators explain the difference. Before comparing statistics from two tools, check their sample/population defaults and handling of missing values.
Find the noise you can reduce
Explore practical variance reduction methods, including metric design and CUPED, and the conditions that make each approach useful.
Read the Variance Reduction Guide
Use standard errors for experiment uncertainty
Distinguish users from estimated means
Standard deviation describes how users' outcomes vary. Standard error describes how an estimator would vary across repeated samples. For an independent sample, the estimated standard error of its mean is s / √n.
Suppose a user-level task-time metric has a sample standard deviation of 20 seconds among 10,000 independent users. The estimated standard error of the mean is 20/√10,000 = 0.2 seconds. Large differences between users coexist with a much smaller uncertainty around their average.
Collecting four times as many independent users would halve this standard error if the outcome variance stayed similar. It would not mechanically halve user-level standard deviation. The individual experience remains variable even as the estimated mean becomes more precise.
Combine arm variances before taking the square root
For independent treatment and control groups, a simple unadjusted difference in means has estimated variance s²ₜ/nₜ + s²꜀/n꜀. Its standard error is the square root of that sum. You do not add the two arm standard deviations and call the result the experiment's uncertainty.
Take a separate synthetic summary with 10,000 independent users in each arm and a 20-second standard deviation in both. The variance of the estimated difference is 400/10,000 + 400/10,000 = 0.08 seconds squared. Its standard error is √0.08, about 0.283 seconds.
If the observed treatment-minus-control difference is −1 second, an illustrative large-sample, unadjusted 95% normal interval is −1 ± 1.96 × 0.283, approximately −1.554 to −0.446 seconds. Negative values mean faster completion under this metric definition.
This example assumes independent groups and an appropriate large-sample approximation. It is not the exact output of GrowthBook's relative-lift, sequential, covariate-adjusted, or ratio-metric methods. GrowthBook's statistical details describe how the platform estimates effects and their uncertainty.
Avoid interpreting intervals as outcome ranges
That interval concerns the mean treatment effect. It does not say that 95% of individual users improved by between 0.446 and 1.554 seconds. An individual user's causal effect is a different quantity, and the experiment does not observe both potential outcomes for the same person.
Likewise, mean ± one standard deviation is not a universal statement about where 68% of users fall. That familiar interpretation depends on an approximately normal distribution. Skewed task times or revenue can look very different.
The frequentist confidence level describes the repeated-sampling coverage of the interval procedure under its assumptions. It is not a posterior probability assigned to a fixed realized interval.
Check what your metric actually measures
Match the variance to the randomization unit
If users are randomized and each contributes many requests, request rows are not automatically independent experimental units. Treating 100 requests from one person as 100 unrelated users can make uncertainty look smaller than it should.
Define the outcome at the intended unit or use a method that accounts for clustering. GrowthBook's aggregate data requirements are relevant when supplying precomputed summaries: the sample size, sum, and dispersion must correspond to the statistic and analysis method.
Two teams can report different standard deviations without either making an arithmetic mistake. One may summarize per-request latency while the other summarizes each user's average latency. They are describing different outcome distributions. Clarify the grain before comparing their numbers.
Paired measurements need another distinction. If the same users provide before-and-after outcomes, the measurements within each pair can be correlated. Analyze each user's difference or use an appropriate paired method; do not apply the independent-arm formula automatically. The paired-means framework treats those differences as the observations of interest. Pairing also does not remove time trends or establish causality by itself; the design still determines which comparison is defensible.
Inspect tails instead of deleting them casually
Large observations influence both variance and standard deviation. A few unusually long tasks can dominate a time metric; a few large purchasers can dominate revenue. Determine whether those values reflect valid behavior, instrumentation defects, retries, or unit errors.
A valid expensive user is not automatically an outlier to discard. Removing high values after seeing an experiment result can change the estimand and introduce selective analysis. Decide justified exclusions or transformations before interpreting the test.
Report complementary summaries when they answer useful questions: a histogram, median, or a relevant tail percentile can show what a single spread measure hides. Those summaries do not replace the mean effect if the mean remains the decision metric.
Keep units consistent across pipelines
Converting seconds to milliseconds multiplies every value by 1,000. Standard deviation also multiplies by 1,000, but variance multiplies by 1,000,000. A dramatic numerical variance increase can therefore be a unit conversion rather than a product regression.
For the four-value example, sample standard deviation becomes approximately 1,633 milliseconds and variance becomes approximately 2,666,667 milliseconds squared. The underlying task times have not changed. Store units with metric definitions and inspect them when comparing dashboards.
A lower variance caused solely by rescaling a metric does not create more statistical information. The effect size rescales too. Separate a numerical change in units from an actual improvement in the precision of your estimate.
Also avoid averaging standard deviations from daily reports and labeling the result a weekly standard deviation. The complete-period spread depends on both variation within days and differences between daily means. Keep the underlying observations or sufficient summaries that support the intended calculation. A simple average of displayed daily values generally loses information needed to reconstruct the full distribution.
Turn spread into a better testing plan
Define the outcome before planning the sample
Start with the metric definition: population, identity, aggregation, window, units, exclusions, and desired effect. Estimate variability from representative historical data at that same grain.
Historical variance is an input, not a guarantee. New traffic, a different user mix, or a changed measurement window can alter it. Explore plausible variance and effect-size assumptions rather than treating one historical number as exact.
Keep the two arm variances separate when appropriate. A treatment can change variability as well as its mean. The simple equal-standard-deviation illustration above is a teaching assumption, not a requirement that production groups have identical spread.
Reduce estimation noise without hiding behavior
CUPED and post-stratification use suitable pre-experiment information and covariates to improve effect estimation. CUPED stands for Controlled-experiment Using Pre-Experiment Data. Its value depends on useful predictive information and a valid implementation.
Adjustment can reduce uncertainty in the treatment effect without making real users' outcomes less variable. That distinction prevents a reporting error: a narrower adjusted interval is not evidence that customers now experience a more consistent product.
Also separate missing data from genuine zeros. A user with no purchase may correctly contribute zero revenue; a missing instrumentation event might represent an unknown outcome. Treating both identically can distort the mean and its variance before any statistical engine sees the data.
Report the right quantity for the decision
| Reader's question | Useful quantity |
|---|---|
| How much do individual outcomes differ? | Standard deviation, with distribution context |
| What enters this uncertainty calculation? | Variance at the correct analysis unit |
| How precisely is the effect estimated? | Standard error and an appropriate interval |
| Is the measured difference worth acting on? | Effect size, uncertainty, guardrails, and decision context |
For a clear experiment readout, show the metric's unit and analysis grain, the estimated effect, and its appropriate uncertainty interval. Add outcome spread when it helps readers understand the population or diagnose a noisy metric.
GrowthBook's experimentation platform brings defined metrics and statistical analysis together. A trustworthy interpretation still starts with your measurement choices: describe spread with standard deviation, calculate with variance where required, and judge precision using the estimator's uncertainty.
Build a clearer experiment readout
Connect your metrics to GrowthBook and evaluate effects with the analysis method and measurement definitions your team has chosen.
Get Started With ExperimentationCatching 80 defects means little until you know how many defects existed.
Suppose a detector flags 125 suspicious software changes. Engineers inspect them and confirm 80 real defects. That tells you something about alert quality, but it does not tell you how many defects the detector missed. To calculate true positive rate, you need the actual positives outside the alert queue too.
True positive rate, or TPR, measures the share of actual positive cases that a system correctly identifies. It is also called recall or sensitivity. The formula is TP / (TP + FN), where TP means true positives and FN means false negatives.
The arithmetic is simple. Choosing the right population, labels, threshold, and denominator is the difficult part. This guide works through an engineering example, distinguishes neighboring metrics, and explains what changes when the “test” is an A/B experiment rather than an item classifier.
Start with actual positives, not alerts
Define the positive class
“Positive” means the condition you want to detect. It does not necessarily mean a good outcome. In a defect detector, positive means a real defect. In a spam filter, it means spam. Write that definition before calculating anything.
A true positive is an item that is actually positive and predicted positive. A false negative is actually positive but predicted negative. Those two groups together form the denominator: all actual positives in the evaluation sample.
The recall definition is therefore conditional on actual class membership. TPR asks, “Among the real defects, what proportion did we catch?” It does not ask, “Among the alerts, what proportion were real?”
Fix the unit and observation window
Choose a consistent evaluation unit. A code change, an individual finding, and a repository are different units. If one change generates 12 duplicate alerts, counting alerts in the numerator and changes in the denominator produces a meaningless rate.
For the example, each unit is one code change, evaluated once by one fixed detector version at one fixed threshold. An independent review process eventually labels each change as defective or not defective. The observation window is long enough for those labels to mature.
This resembles the discipline needed when defining experiment metrics: the event, identity, time window, and denominator must describe the same question. A correct formula applied to incompatible counts still gives the wrong answer.
Calculate TPR from the confusion matrix
Put every evaluated item in one cell
Consider an original synthetic evaluation of 1,000 code changes. Reviewers establish that 100 contain a defect and 900 do not. The detector catches 80 defects, misses 20, and incorrectly flags 45 nondefective changes.
| Actual label | Predicted positive | Predicted negative | Total |
|---|---|---|---|
| Defective | 80 true positives | 20 false negatives | 100 |
| Not defective | 45 false positives | 855 true negatives | 900 |
| Total | 125 | 875 | 1,000 |
Read the labels rather than relying on cell position. Libraries and reports can arrange axes differently. The scikit-learn confusion matrix convention uses actual classes on rows and predicted classes on columns, with class order also affecting where each value appears.
Check the totals before calculating rates. The four cells sum to 1,000. True positives plus false negatives sum to 100 actual defects. Predicted positives total 125, which is a different denominator.
Divide detected positives by all actual positives
The calculation is:
TPR = TP / (TP + FN) = 80 / (80 + 20) = 80 / 100 = 0.80 = 80%.
Interpret it in words: the detector caught 80% of the actual defects in this evaluation sample. It missed 20%, so the false negative rate is 20%. For a binary task with complete labels, TPR and false negative rate sum to 100%.
Do not divide 80 by 1,000. That would measure true positives as a share of all evaluated changes, not recall. Do not divide 80 by 125 either. That is precision, which answers the alert-quality question.
The result describes this sample and threshold. It does not establish that every future workload will have the same recall, or that all defect categories are equally detectable. Keep the counts beside the percentage so readers can see the support behind it.
Handle zero positive support explicitly
If the sample contains no actual defects, TP + FN equals zero. Sample TPR is undefined. Returning 0% would imply the detector missed known defects; returning 100% would imply it successfully found them. Neither interpretation is supported.
Software libraries need a practical zero-division convention. That convention can return a placeholder or warning, but it does not create evidence. Report “TPR unavailable: zero actual positives” and preserve the support count. A pipeline can then distinguish an unevaluable slice from measured poor performance.
Check the experiment error risks
See how power, repeated checks, and multiple comparisons affect the reliability of experimental decisions beyond a single classifier score.
Read the False Positives GuideCompare TPR with precision and false positives
Keep the denominators visible
For the same 1,000 changes, the other common metrics tell different stories:
| Metric | Calculation | Result |
|---|---|---|
| True positive rate / recall | 80 / 100 actual positives | 80% |
| Precision | 80 / 125 predicted positives | 64% |
| False positive rate | 45 / 900 actual negatives | 5% |
| Specificity | 855 / 900 actual negatives | 95% |
| Accuracy | (80 + 855) / 1,000 changes | 93.5% |
The classification metric definitions differ because they condition on different populations. Here, 36% of alerts are false, but the false positive rate is 5%. Calling both numbers “the false positive rate” hides the operational distinction.
Accuracy can also look reassuring in an imbalanced population. A detector that labels all 1,000 changes nondefective would be 90% accurate while detecting none of the 100 defects. TPR exposes that failure, though it still needs the false-positive side of the picture.
Choose a threshold for the decision
Suppose a stricter threshold, evaluated on the same labeled changes, catches 60 defects and incorrectly flags 10 nondefective changes. Its TPR is 60%, its precision is about 85.7%, and its false positive rate is about 1.11%.
This threshold reduces the review queue from 125 changes to 70. It also misses 40 defects instead of 20. Whether that tradeoff is acceptable depends on the consequences of a missed defect, the review team's capacity, and what action follows an alert.
A ROC curve plots TPR against false positive rate across thresholds. It helps compare operating points. It does not pick the operational threshold for you, and an area-under-curve summary does not specify the queue size at the threshold you intend to deploy.
If you tune thresholds using the same labels you later report as a final evaluation, the score can be optimistic. Use separate validation and held-out evaluation data, or another defensible evaluation design, and freeze the decision rule before the final assessment.
Specify the action at each threshold too. A low-confidence finding might create a suggestion, while a high-confidence finding might block a merge. Those are different interventions with different error costs. One aggregate recall figure should not imply that every detected item received the same treatment.
Make sure the labels can support the claim
Audit predicted negatives too
If engineers review only alerts, you can estimate precision within the reviewed alert population. You cannot directly count missed defects among unreviewed negatives. Assuming those changes are clean makes FN artificially small and TPR artificially high.
An evaluation may need independent review of a representative sample of predicted negatives, delayed incident labels, or a curated benchmark with known defects. Each approach has limits. Incident labels miss undetected failures; curated examples can differ from ordinary production work.
If the evaluation oversamples suspicious changes or certain repositories, report that design. Use appropriate weighting when estimating performance for the real workload. A simple unweighted confusion matrix describes the sampled dataset, which may not represent the deployed population.
Preserve uncertainty and segment support
An 80% estimate based on 4 detected defects out of 5 is much less precise than one based on 800 out of 1,000. The important sample size for recall is the count of actual positives, not just the total number of changes evaluated.
Report positive support by important slices, such as language, defect category, or repository type. A high pooled TPR can hide weak performance on a rare but expensive failure mode. At the same time, avoid declaring tiny slices definitively better or worse from a few examples.
Counts alone do not make observations independent. Many similar changes from one repository can share a common pattern. Use uncertainty methods that fit the evaluation design rather than assuming every row is an unrelated trial.
Separate label delay from label absence
A recently deployed change might appear nondefective because nobody has had time to find the bug. Treating it as a confirmed negative gives the newest cohort a different labeling standard from older cohorts.
Define when an item becomes eligible for evaluation and track unresolved labels separately. Keep model version, threshold, label version, and observation dates with the result. That makes a later score change interpretable: you can tell whether the detector changed, the workload changed, or the labels matured.
For a warehouse-native measurement workflow, these definitions belong in the underlying data model. Joining model decisions to review outcomes still requires identity and time alignment; a queryable warehouse does not automatically resolve ambiguous ground truth.
Keep comparable populations together
When comparing detector versions, evaluate them on the same held-out cases where possible. Otherwise, an apparent recall improvement could come from an easier mix of defects. Preserve a breakdown of new, removed, and unchanged cases so reviewers can see what changed between evaluations.
When combining reports, add the underlying TP and FN counts for nonoverlapping, consistently defined populations, then calculate the pooled rate. An unweighted average of repository percentages answers a different question because it gives a tiny repository the same influence as a large one. Either summary can be useful, but label the weighting explicitly.
If the same change appears in several reports, deduplicate or model that repeated observation before pooling. Repeated scans do not create additional independent defects. Keep an audit trail from the final counts back to the evaluated item identifiers so a suspicious rate can be investigated.
Connect detection quality to product decisions
Distinguish classifier recall from statistical power
In hypothesis testing, statistical power is the probability that a testing procedure rejects the null when a specified alternative is true. It is a repeated-test detection probability, depending on assumptions such as effect size, variance, sample size, and decision rule.
That is related to the idea of detecting a real signal, but it is not the same dataset as a labeled-item confusion matrix. In an ordinary A/B program, you do not observe the true causal effect of every experiment with certainty. Dividing declared winners by all experiments does not reveal a ground-truth TPR.
Power planning asks how often your procedure would detect a particular real effect under its assumptions. A classifier evaluation asks how many labeled positives were detected in the evaluated sample. Keep those questions separate when discussing GrowthBook's statistical methods.
Evaluate the intervention as well as the detector
An improved offline detector can still create a worse product workflow. More alerts might overwhelm engineers, delay releases, or catch defects too late to matter. Conversely, a modest recall change might be valuable if it catches the failures with the highest operational cost.
When appropriate, use randomized experiments to evaluate the downstream intervention: which review experience helps teams resolve defects, with what delay and cost? Keep detector recall, review workload, and product outcomes as separate measures.
GrowthBook's experimentation platform can analyze defined outcomes from such tests. Your team supplies reliable assignment, exposure, labels, and metric definitions. Neither a high TPR nor a significant product result eliminates the need to inspect the other evidence.
Use a repeatable reporting checklist
Before sharing a TPR number, state the positive class, evaluation unit, detector version, threshold, and label process. Show TP and FN beside the rate. Include precision or false positive rate so readers can assess the workload created by detection.
Then ask whether predicted negatives were evaluated, whether labels had time to mature, and whether the sample reflects the population where you will act. Mark zero-support slices unavailable and explain material gaps in coverage.
The useful result is a statement someone can audit: “At this threshold, the detector found 80 of 100 labeled defects in these 1,000 evaluated changes, while producing 45 false alerts.” That is a clearer basis for a decision than “our model is 80% accurate.”
Measure what the change delivers
Connect your data and defined metrics to GrowthBook to evaluate how a product or workflow change affects the outcomes your team cares about.
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