Regression vs. ANOVA discrepancy (aov vs lm in R) I was always under the impression that regression is just a more general form of ANOVA and that the results would be identical. Recently, however, I have run both a regression and an ANOVA on the same data and the results differ significantly. That is, in the regression model both main effects and the interaction are significant, while in the ANOVA one main effect is not significant. I expect this has something to do with the interaction, but it's not clear to me what is different about these two ways of modeling the same question. If it's important, one predictor is categorical and the other is continuous, as indicated in the simulation below. 
Here is an example of what my data looks like and what analyses I'm running, but without the same p-values or effects being significant in the results (my actual results are outlined above):
group<-c(1,1,1,0,0,0)
moderator<-c(1,2,3,4,5,6)
score<-c(6,3,8,5,7,4)

summary(lm(score~group*moderator))
summary(aov(score~group*moderator))

 A: The summary function calls different methods depending on the class of the object.  The difference isn't in the aov vs lm, but in the information presented about the models.  For example, if you used anova(mod1) and anova(mod2) instead, you should get the same results.  
As @Glen says, the key is whether the tests reported are based on Type 1 or Type 3 sums of squares.  These will differ when the correlation between your explanatory variables is not exactly 0.  When they are correlated, some SS are unique to one predictor and some to the other, but some SS could be attributed to either or both.  (You can visualize this by imagining the MasterCard symbol--there's a small region of overlap in the center.)  There is no unique answer in this situation, and unfortunately, this is the norm for non-experimental data.  One approach is for the analyst to use their judgment and assign the overlapping SS to one of the variables.  That variable goes into the model first.  The other variable goes into the model second and gets the SS that looks like a cookie with a bite taken out of it.  It's effect can be tested by what is sometimes called $R^2$ change or F change.  This approach uses Type 1 SS.  Alternatively, you could do this twice with each going in first, and report the F change test for both predictors.  In this way, neither variable gets the SS due to the overlap.  This approach uses Type 3 SS.  (I should also tell you that the latter approach is held in low regard.)
Following the suggestion of @BrettMagill in the comment below, I can try to make this a little clearer.  (Note that, in my example, I'm using just 2 predictors and no interaction, but this idea can be scaled up to include whatever you like.)
Type 1: SS(A)   and SS(B|A)
Type 3: SS(A|B) and SS(B|A)
A: The results from the aov output are giving you probabilities based on Type 1 sum of squares.  This is why the interaction result is the same and the main effects differ.
If you use probabilities based on Type 3 sum of squares then they will match the linear regression results.
library(car)
Anova(aov(score~group*moderator),type=3)

A: The main difference between linear regression and ANOVA is, in ANOVA the predictor variables are discrete (that is they have different levels). Whereas in linear regression, the predictor variables are continuous. 
