Lesson 1: The anatomy of the results page
In this lesson, you learn how the results page is organized and the basic logic that connects its three sections. You also learn why a single experiment can produce many individual results and how Confidence helps you navigate them.
When you open the results page for an experiment in Confidence, the first thing to notice is that it has a clear structure. There are three sections, stacked from top to bottom: Spotlight, Health checks, and Metrics. Each section serves a different purpose. Understanding what each one answers is the key to reading results efficiently.
The three sections
Spotlight
The Spotlight is at the very top of the page. It gives you an overall recommendation for each treatment variant: whether to ship, continue, end, or stop the experiment. Think of the Spotlight as a summary that synthesizes everything below it into a single actionable signal.
You do not need to read every metric result to know whether your experiment is worth shipping. The Spotlight does that job. Later in this course you will learn exactly what drives each possible recommendation.
Health checks
The health checks section serves two related purposes. First, it verifies whether the experiment itself is trustworthy: that traffic is flowing as expected and that groups are balanced. Second, it monitors whether any metric is moving in the wrong direction, which can trigger an Abort recommendation even when the experiment is technically set up correctly.
In other words, health checks are not only about data quality. They are also an active monitoring layer that can tell you the treatment variant is causing harm before you have finished collecting data.
Metrics
The Metrics section shows the results for each individual metric, for each treatment variant compared to the control variant. This is where you see the actual numbers: the estimated effect, confidence intervals, and whether the result is statistically significant.
Many results from one experiment
A single experiment can produce a large number of individual results. If you have T treatment variants (not counting the control variant) and M metrics, you have T × M individual results to interpret. For example, an experiment with two treatment variants and four metrics gives you eight results.
An experiment tests two new versions of a checkout flow (version A and version B) against the current version (control variant), using four metrics: conversion rate, time in checkout, number of errors, and customer satisfaction score.
That gives 2 treatment variants × 4 metrics = 8 individual results to interpret.
This is one reason the Spotlight is so useful: it collapses those eight results into two recommendations, one per treatment variant.
How the sections connect
The three sections work together. Health checks feed into the Spotlight: if a health check fails, the Spotlight takes that into account. The Metrics section provides the raw signal that the Spotlight summarizes. As you work through the course, you will learn each section in detail. By the end, you will be able to trace any Spotlight recommendation back to the individual numbers that produced it.