Platform

Developers

Resources

Pricing

Type 2 Error

A Type 2 error, also known as a false negative, occurs in statistical hypothesis testing when a null hypothesis is not rejected when it is, in fact, false. In other words, it's the error of failing to reject a false null hypothesis.

Example

Let's consider a pharmaceutical company testing a new drug. The null hypothesis (H0) might be that the new drug has no effect on patients. The alternative hypothesis (H1) would be that the drug does have an effect on patients.

A Type 2 error would occur if the clinical trials conclude that the drug has no effect (i.e., they fail to reject the null hypothesis), when in reality, the drug does have an effect (i.e., the null hypothesis is false).

Context in Multi-arm Experiments

In the context of multi-arm experiments, as mentioned in the provided text, there's a trade-off between Type I and Type 2 errors. If you want to be cautious and maintain your Type I error rates at 5%, you can use a Bonferroni correction, but you should realize that you're increasing your Type 2 error rates.

Significance

The probability of making a Type 2 error is often denoted by β. The power of a test, which is the probability of correctly rejecting a false null hypothesis, is calculated as 1 - β. Therefore, the higher your risk of a Type 2 error, the lower the power of your test.

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