by ABC Analytics
Last Updated August 01, 2020 09:19 AM - source

In a textbook, it introduces the chi-square goodness of fit test to test independence between two variables (organized in a contingency table), which can also be used to test if m samples are likely to come from the same distribution by using one dimension as samples and the other as values/bins.

Then in another chapter, it introduces the Mann-Whitney test, which seems to do the same thing. Both are nonparametric and chisquare can be applied to continuous distribution by discretizing the bins.

I am confused that they seem to do the same thing, so for the purpose of testing if two populations are the same (distribution and parameters), what're their differences?

- Serverfault Help
- Superuser Help
- Ubuntu Help
- Webapps Help
- Webmasters Help
- Programmers Help
- Dba Help
- Drupal Help
- Wordpress Help
- Magento Help
- Joomla Help
- Android Help
- Apple Help
- Game Help
- Gaming Help
- Blender Help
- Ux Help
- Cooking Help
- Photo Help
- Stats Help
- Math Help
- Diy Help
- Gis Help
- Tex Help
- Meta Help
- Electronics Help
- Stackoverflow Help
- Bitcoin Help
- Ethereum Help