Best way to compare two treatment groups to a control I'm trying to compare two different fertilizer treatments to a control group (no fertilizer). I ran a model like this:
plants_lm <- lm(weight ~ group, data = plants)

summary(plants_lm)

Call:
lm(formula = weight ~ group, data = plants)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.0710 -0.4180 -0.0060  0.2627  1.3690 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)        5.0320     0.1971  25.527   <2e-16 ***
groupFertlizer_A  -0.3710     0.2788  -1.331   0.1944    
groupFertlizer_B   0.4940     0.2788   1.772   0.0877 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.6234 on 27 degrees of freedom
Multiple R-squared:  0.2641,    Adjusted R-squared:  0.2096 
F-statistic: 4.846 on 2 and 27 DF,  p-value: 0.01591

I'm a little confused as to how to interpret these results. The only significant result is the control. What does this mean exactly? I feel like I'm doing something wrong, because what I want to know is whether the other two groups are different from the control. Hope this makes some sense.

This question comes up a lot and there is a method that answers all the questions you have. Look at Ways of comparing linear regression interepts and slopes?. It explains how to compare slopes and intercepts of as many groups as you want and will tell you the difference between groups.
