Type 1 Error

A Type 1 error, also known as a "false positive," is a kind of statistical error that occurs when a hypothesis test incorrectly rejects a true null hypothesis. In other words, it's the error of accepting an alternative hypothesis (the real hypothesis of interest) when the results can be attributed to chance.

Plainly speaking, a Type 1 error is detecting an effect that isn't present.

Example

Let's say you're running an A/B test on your website to determine if a new feature increases user engagement. Your null hypothesis (H0) is that the new feature has no effect on user engagement, and your alternative hypothesis (H1) is that the new feature does affect user engagement.

  • If you conclude that the new feature increases user engagement when it actually does not, you've made a Type 1 error. You've incorrectly rejected the null hypothesis.

Context in Multi-arm Experiments

In the context of multi-arm experiments, Type 1 error rates are most suitable when:

  1. You have prior knowledge or data that the control group is suboptimal.

  2. The real objective of the experiment is to determine the best test group.

  3. Your team/company is already committed to making a change.

However, you don't want to be at the mercy of statistical noise, which can thrash your user experience, trigger unknown secondary effects, and/or create extra product work. As a rule of thumb, if you use ⍺=0.05 (a common threshold for Type 1 error), you should feel comfortable running up to 4 variations. This slightly biases you towards making a change, but keeps the overall Type 1 error rate below 0.05.

Join the #1 experimentation community

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