Timeline for Deciding between chi square and t test
Current License: CC BY-SA 3.0
6 events
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Nov 24, 2017 at 22:44 | comment | added | whuber | +1. This is a good account. Out of an abundance of caution--never to be deplored!--you have enunciated overly restrictive assumptions, but they are accurately and well stated. And your remarks about subtleties and misconceptions are to the point: the choice of tests is never as simple as suggested by the preceding comment by@octern, because the nature of the data is one of the least important factors in the decision. The purpose of testing is the primary desideratum; next is a choice of statistical model for the experiment or data; and everything follows from those. | |
Jul 23, 2017 at 6:48 | review | Suggested edits | |||
Jul 23, 2017 at 8:29 | |||||
Aug 1, 2016 at 23:49 | review | Suggested edits | |||
Aug 2, 2016 at 6:43 | |||||
Nov 14, 2013 at 22:57 | comment | added | octern | This is correct, though I think the core of it is actually simple. Use chi-square if your predictor and your outcome are both categorical variables (e.g., purple vs. white). Use a t-test if your predictor is categorical and your outcome is continuous (e.g., height, weight, etc). Use correlation or regression if both the predictor and the outcome are continuous. | |
Nov 14, 2013 at 18:40 | vote | accept | biogirl | ||
Nov 14, 2013 at 18:32 | history | answered | A. Kennard | CC BY-SA 3.0 |