Platform

Developers

Resources

Pricing

Lean Hypothesis Testing

Lean Hypothesis Testing is a method used in product development and business strategy to validate assumptions and make informed decisions. It is a key component of the Lean Startup methodology and is designed to ensure that businesses are building products that meet the needs of their customers.

The process involves formulating a hypothesis, designing an experiment to test it, running the experiment, and then analyzing the results to either validate or invalidate the hypothesis. The goal is to learn as much as possible about the customer's needs and preferences, and to use this knowledge to guide product development.

Here's a detailed breakdown:

1. Formulate a Hypothesis

A hypothesis is a proposed explanation for a phenomenon, made as a starting point for further investigation. In the context of Lean Hypothesis Testing, a hypothesis is composed of three elements: Action, Predicted Outcome, and Rationale.

Action: If we reduce the number of fields on the sign-up page…

Predicted outcome: …then the percentage of users that complete signup will increase…

Rationale: …because they have to spend less time and effort to see value in the product

2. Design an Experiment

Once the hypothesis is formulated, the next step is to design an experiment to test it. This could be an A/B test, a user survey, a prototype, or any other method that can provide data to validate or invalidate the hypothesis.

3. Run the Experiment

The experiment is then executed. This could involve exposing different versions of a product feature to different segments of users, distributing a survey, or conducting user interviews.

4. Analyze the Results

The data collected from the experiment is then analyzed to determine whether the hypothesis is validated or invalidated. This involves statistical analysis and may also involve qualitative analysis of user feedback.

5. Learn and Iterate

Regardless of whether the hypothesis is validated or invalidated, there is always something to learn. These learnings can then be used to formulate new hypotheses and the cycle begins again.

Lean Hypothesis Testing is a powerful tool for reducing risk and uncertainty in product development. By validating assumptions before committing significant resources, businesses can avoid costly mistakes and ensure that they are building products that truly meet the needs of their customers.

Join the #1 Community for Product Experimentation

Connect with like-minded product leaders, data scientists, and engineers to share the latest in product experimentation.

Try Statsig Today

Get started for free. Add your whole team!

What builders love about us

OpenAI OpenAI
Brex Brex
Notion Notion
SoundCloud SoundCloud
Ancestry Ancestry
At OpenAI, we want to iterate as fast as possible. Statsig enables us to grow, scale, and learn efficiently. Integrating experimentation with product analytics and feature flagging has been crucial for quickly understanding and addressing our users' top priorities.
OpenAI
Dave Cummings
Engineering Manager, ChatGPT
Brex's mission is to help businesses move fast. Statsig is now helping our engineers move fast. It has been a game changer to automate the manual lift typical to running experiments and has helped product teams ship the right features to their users quickly.
Brex
Karandeep Anand
President
At Notion, we're continuously learning what our users value and want every team to run experiments to learn more. It’s also critical to maintain speed as a habit. Statsig's experimentation platform enables both this speed and learning for us.
Notion
Mengying Li
Data Science Manager
We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion.
SoundCloud
Don Browning
SVP, Data & Platform Engineering
We only had so many analysts. Statsig provided the necessary tools to remove the bottleneck. I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig.
Ancestry
Partha Sarathi
Director of Engineering
We use cookies to ensure you get the best experience on our website.
Privacy Policy