Curated library of real product experiments

Browse a structured repository of experiments run by real teams, with goals, designs, outcomes, and methodological commentary. Use it as case material for your product team or research.

50 experiments in full dataset Find the most significant results Expert commentary Behavioral theory

Why Experiment?

Randomization Visualizer

See how randomization controls for confounding variables. Click the "Re-randomize" button and watch the confounding variables (both known and unknown) balance across groups through random assignment. This keeps your estimate of the treatment effects unbiased.

Young

Old

Unknown Variable

● Male

■ Female

★ High motivation

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Experiment Library (preview)

Unlock the full dataset

One‑time purchase for the complete library, delivered as a clean, structured CSV.

50 experiments across SaaS, consumer, and marketplace products.
Columns for product, sector, experiment type, goal, metric, design, sample size, outcome, and decision.
Methodological commentary: threats to validity, design notes, and analysis hints.
Ready for use in R, Python, SQL, or BI tools.

Download full library (CSV)

Want early access or a custom export? Email: jared@productscience.consulting

Schema preview

Every experiment in the dataset follows the same schema so you can run meta‑analysis or build your own internal repository on top.

Column	Type	Description
experiment_id	string	Stable identifier (e.g., E‑001)
product_name	string	Name or anonymized label of the product
sector	string	Sector tag (SaaS, consumer, marketplace, etc.)
goal	string	Primary business goal (activation, retention, monetization, etc.)
primary_metric	string	Main outcome metric tracked
experiment_type	string	A/B test, fake door, quasi‑experiment, user research, etc.
design_summary	string	Short description of the intervention and control
sample_size	integer	Total sample or per‑arm counts where available
outcome_summary	string	Key outcome and magnitude of effect
p_value	float	significance in effect for primary metric
decision	string	Product or business decision made from the result
methodology_notes	string	Internal commentary on validity, design choices, and caveats

How teams use this library

Product managers, data scientists, and researchers use this as a reference when planning new experiments, teaching methodology, or quantifying patterns across many tests.

Where do these experiments come from?

The library curates experiments from public case studies, talks, and research reports, then standardizes them into a consistent schema with added methodological notes.

Can I use this in teaching or training?

Yes. The commentary is designed to be used as case material for internal training, workshops, or university courses. You can slice the dataset by sector, goal, or method.

Will you add my experiments?

If you run a substantial experiment program and want anonymized entries included, reach out. Contributions can be credited and included in future dataset releases.