Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

The Adoption of T-test Instead of Z-test Might Improve the Accuracy #445

Open
zayn7lie opened this issue Apr 15, 2023 · 1 comment
Open

Comments

@zayn7lie
Copy link

We cannot know the real σ (Standard Deviation), because the standard deviation we get is from the data we get from users. Then we have to consider df (Degree of Freedom) to the test for σ-hat. T-test could make the result more accurate because it make df into consideration while z-test only exist when df tends to infinity or σ is ideally known.
Thus, the adoption of T-test is better then Z-test.

@GalvinGao
Copy link
Member

GalvinGao commented Apr 17, 2023

In fact, the Z-test functionality was omitted when we were implementing the v3 backend last year, due to the exceptional and tremendous amount of load it puts onto our infrastructure at that time. We were planning to reimplement and further, integrate it with our monitoring and alerting system in order to give us the right signal to dive into deeper investigations on a potential deviation of the dataset.

Keeping the issue for tracking. Thanks a lot for the precious feedback!

@zayn7lie zayn7lie changed the title The Adoption of T-test Instead of Z-test Might Inprove the Accuracy The Adoption of T-test Instead of Z-test Might Improve the Accuracy Apr 17, 2023
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants