Experiment Tracking System Notion Template

Overview

A structured system for teams and individuals running experiments — whether for machine learning model development, product A/B testing, or scientific research. This template provides a centralised log to track hypotheses, model versions, performance metrics, and risk levels across every experiment.

Key Features

• Quick Action Buttons — log a new experiment or update an existing one instantly
• Quick Notes Page — capture observations, hypotheses, and informal experiment ideas
• Navigation Panel with filtered views:
• Model Version
• Status
• Risk Level
• Performance Metric
• Archive
• Bin
• Dashboard Summary Database — inline view of ongoing experiments
• Full Experiment Database — complete log of all experiments with detailed properties

What's Inside

Dashboard Layout

Two-column layout:

• Left column: Quick Action callout, Quick Notes link, Navigation panel with 6 filtered views, Database shortcut
• Right column: Inline summary database with current experiment overview

Databases Included

• Experiment Overview Database — inline summary filtered to active or recent experiments
• Full Experiment Database — all experiments with:
• Experiment name and description
• Hypothesis
• Model version (for ML experiments)
• Status (Planning, Running, Completed, Failed, Archived)
• Risk level (Low, Medium, High)
• Performance metric (Accuracy, Revenue, Engagement, etc.)
• Results and conclusions
• Notes and tags

Navigation Views

• Model Version — tracks iterations of models or experiment configurations
• Status — filters by Planning, Running, Completed, or Failed
• Risk Level — segments experiments by potential downside
• Performance Metric — groups by the KPI being measured
• Archive — completed and concluded experiments
• Bin — cancelled or invalid entries

Use Cases

• Tracking machine learning model training runs and comparing accuracy metrics
• Logging A/B tests for product features with hypothesis, variant, and result
• Managing scientific research experiments with methodology and confidence tracking
• Running growth experiments (pricing, onboarding flows, email campaigns) with risk assessment
• Building a team knowledge base of experiment outcomes to avoid repeating failures

Why You'll Love It

• Structured experiment logging prevents duplicate work and missed learnings
• Risk Level tracking helps teams prioritise safe experiments before high-risk ones
• Model Version filtering is directly relevant for ML/AI teams iterating on models
• Performance Metric view provides a quick benchmark comparison across experiments
• Archive keeps completed experiments accessible for future reference without cluttering active views
• Works equally well for product, data science, and academic research teams

Benefits

• Institutional Memory — all experiment outcomes preserved and searchable
• Risk Awareness — risk level tracking prevents high-impact failures from going unplanned
• Reproducibility — detailed logs allow experiments to be reproduced or referenced
• Collaboration — shared Notion workspace lets teams see what's running and what concluded
• Speed — quick action buttons accelerate experiment logging during fast-paced sprints

Who It's For

• Data Scientists and ML Engineers iterating on model versions
• Product Managers running feature experiments and A/B tests
• Growth Teams testing acquisition and retention strategies
• Academic Researchers tracking research study parameters and results
• R&D Teams in startups and enterprises documenting innovation cycles

How It Works

1. Create an Experiment — use Quick Action to log a new experiment with hypothesis
2. Set Properties — assign model version, risk level, performance metric, and status
3. Run the Experiment — update status to "Running" and log progress in notes
4. Record Results — once complete, enter outcomes and update status to "Completed" or "Failed"
5. Review by Metric — use Performance Metric view to compare results across experiments
6. Risk Review — use Risk Level filter to ensure high-risk experiments have proper safeguards
7. Archive Learnings — move concluded experiments to Archive with full notes intact
8. Reference Past Work — search archived experiments before starting new ones to avoid duplication

Frequently Asked Questions

Summary

The Experiment Tracking System Notion template brings rigour and clarity to the experimentation process. By capturing hypotheses, risk levels, model versions, and performance metrics in a structured database, teams can iterate faster, learn from past results, and build a searchable knowledge base of every experiment ever run. An essential tool for data science teams, product managers, and researchers who take experimentation seriously.