This question is about the way to go with a very common statistical model: Continuos dependent and categorical independent variable. In my stats class I learned to use the ANOVA: check assumptions, conduct ANOVA, and if significant, do post-hoc tests. Now for the post-hoc tests I have seen several ways how people conduct them regarding p-value correction: One camp basically always applies some form of adjustment method, the second camp mostly states: "I am doing orthogonal contrasts, no adjustment needed".
anova(lm(Sepal.Length ~ Species, data = iris)) pairwise.t.test(iris$Sepal.Length, iris$Species)
Only later when I was introduced to R, I learned about linear models (LM). By now I much prefer those, easier to set up, easier to interpret, way more flexible.
summary(lm(Sepal.Length ~ Species, data = iris))
What I don't understand is why people do the extra step of conducting an ANOVA on top, when the LM already gives you everything that you need (in most cases). And you don't even have to conduct post-hoc tests, let alone think about p-value adjustments (I guess it's the orthogonal argument that strikes here again).
What is the way to go here? If one is fine with the contrasts the linear model provides you, is there a necessity to still conduct an ANOVA, or would you just interpret the parameter of the LM and leave it here?
And even if I need to compare every group vs every other group (or any other non-orthogonal contrasts) and have a more complicated model like a linear mixed model, can't I just estimate the model and conduct a general linear hypotheses test or a least square means test?