---
title: Configure the Experiment Scorecard
description: Record an experiment's hypothesis and choose the primary and secondary metrics Statsig computes for its scorecard in the Statsig console.
product: general
lang: en
token_estimate: 577
---
# Configure the Experiment Scorecard

> For AI agents: a documentation index is available at [/llms.txt](/llms.txt). Append `.md` to any page URL for markdown, or send `Accept: text/markdown`.

An experiment's scorecard documents the hypothesis you're testing and the metrics that measure it. Configure it on the experiment's _Setup_ tab after you [create the experiment](https://docs.statsig.com/experiments/create-new) and before you start it. Statsig computes scorecard metrics daily, and they're eligible for advanced treatments such as [CUPED](https://docs.statsig.com/experiments/statistical-methods/methodologies/cuped) and [sequential testing](https://docs.statsig.com/experiments/advanced-setup/sequential-testing).

The _Setup_ page extends the information you entered when you created the experiment.

## Scorecard fields

- **Hypothesis**: A rich-text field. Use its toolbar to apply headings, bold, italic, underline, strikethrough, links, text color, highlighting, bulleted and numbered lists, blockquotes, images, and code blocks.
- **Primary Metrics**: The metrics you expect the experiment to directly affect.
- **Secondary Metrics**: Metrics you monitor for unintended side effects. They aren't the primary focus of the experiment.

Keep the primary metric set small. For how a focused scorecard changes the way you read results, go to [Best practices and avoiding false positives](https://docs.statsig.com/experiments/interpreting-results/best-practices).

## Write the hypothesis

The _Hypothesis_ field includes a template that you can modify:

> We believe that \[change or feature\] for \[user segment\] will \[desired outcome\] because \[reason or insight\]. We will measure success using \[primary metric\], and monitor \[guardrail metrics\] to ensure no negative impact.

## Hypothesis Advisor

Hypothesis Advisor gives instant feedback on the hypothesis you enter and flags what's missing. Admins can set custom requirements that Statsig uses to guide experimenters toward more complete hypotheses.

This Statsig AI feature is off by default. Enable it from _Settings > Experiment > Project > Statsig AI_. On the same settings page, configure any custom requirements you want Hypothesis Advisor to enforce (for example, "Strongly recommend that a validation plan be mentioned").