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--- duke_notes [2025/11/17 21:01] – admin
+++ duke_notes [2025/11/17 21:46] (current) – admin
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 [[https://stats.libretexts.org/Bookshelves/Introductory_Statistics/OpenIntro_Statistics_(Diez_et_al)./06%3A_Inference_for_Categorical_Data | Inference for Categorical Variables]]
-effect modification vs confounding
+===== General Tips =====
+  * if you can, pick a continuous outcome over a binary outcome
+    * Why? For a binary outcome, you'll need a much larger sample size. Continuous outcomes also allow more precision.
+  * logistic regressions stink!
+logistic regression = linear model for the log-odds of the outcome
+=== analyzing relationship between categorical outcome and a continuous covariate ===
+===effect modification vs confounding===
 if we don't take effect modification into account, we get an over-generalized estimate of the relationship between the outcome and the exposure for the entire co-hort

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