ANOVA vs multiple linear regression? Why is ANOVA so commonly used in experimental studies? ANOVA vs multiple linear regression?
I understand that both of these methods seem to use the same statistical model. However under what circumstances should I use which method?
What are the advantages and disadvantages of these methods when compared?
Why is ANOVA so commonly used in experimental studies and I hardly ever find a regression study?
 A: ANOVA and OLS regression are mathematically identical in cases where your predictors are categorical (in terms of the inferences you are drawing from the test statistic).  To put it another way, ANOVA is a special case of regression.  There is nothing that an ANOVA can tell you that regression cannot derive itself.  The opposite, however, is not true.  ANOVA cannot be used for analysis with continuous variables.  As such, ANOVA could be classified as the more limited technique. Regression, however, is not always as handy for the less sophisticated analyst. For example, most ANOVA scripts automatically generate interaction terms, where as with regression you often must manually compute those terms yourself using the software.  The widespread use of ANOVA is partly a relic of statistical analysis before the use of more powerful statistical software, and, in my opinion, an easier technique to teach to inexperienced students whose goal is a relatively surface level understanding that will enable them to analyze data with a basic statistical package.  Try it out sometime...Examine the t statistic that a basic regression spits out, square it, and then compare it to the F ratio from the ANOVA on the same data.  Identical!
A: The main benefit of ANOVA ovethe r regression, in my opinion, is in the output. If you are interested in the statistical significance of the categorical variable (factor) as a block, then ANOVA provides this test for you. With regression, the categorical variable is represented by 2 or more dummy variables, depending on the number of categories, and hence you have 2 or more statistical tests, each comparing the mean for the particular category against the mean of the null category (or the overall mean, depending on dummy coding method). Neither of these may be of interest. Thus, you must perform post-estimation analysis (essentially, ANOVA) to get the overall test of the factor that you are interested in.
A: It would be interesting to appreciate that the divergence is in the type of variables, and more notably the types of explanatory variables. In the typical ANOVA we have a categorical variable with different groups, and we attempt to determine whether the measurement of a continuous variable differs between groups. On the other hand, OLS tends to be perceived as primarily an attempt at assessing the relationship between a continuous regressand or response variable and one or multiple regressors or explanatory variables. In this sense regression can be viewed as a different technique, lending itself to predicting values based on a regression line.
However, this difference does not stand the extension of ANOVA to the rest of the analysis of variance alphabet soup (ANCOVA, MANOVA, MANCOVA); or the inclusion of dummy-coded variables in the OLS regression. I'm unclear about the specific historical landmarks, but it is as if both techniques have grown parallel adaptations to tackle increasingly complex models.
For example, we can see that the differences between ANCOVA versus OLS with dummy (or categorical) variables (in both cases with interactions) are cosmetic at most. Please excuse my departure from the confines in the title of your question, regarding multiple linear regression. 
In both cases, the model is essentially identical to the point that in R the lm function is used to carry out ANCOVA. However, it can be presented as different with regards to the inclusion of an intercept corresponding to the first level (or group) of the factor (or categorical) variable in the regression model. 
In a balanced model (equally sized $i$ groups, $n_{1,2,\cdots\, i}$) and just one covariate (to simplify the matrix presentation), the model matrix in ANCOVA can be encountered as some variation of:
$$X=\begin{bmatrix} 
1_{n_1} & 0 & 0 & x_{n_1}  & 0   & 0\\
0 & 1_{n_2} & 0 & 0 & x_{n_2} & 0\\
0 & 0 & 1_{n_3} & 0 & 0 &   x_{n_3}
\end{bmatrix}$$
for $3$ groups of the factor variable, expressed as block matrices.
This corresponds to linear model:
$$y = \alpha_i + \beta_1\, x_{n_1}+ \beta_2\,x_{n_2} \,+ \beta_3\,x_{n_3}\,+ \epsilon_i$$ with $\alpha_i$ equivalent to the different group means in an ANOVA model, while the different $\beta$'s are the slopes of the covariate for each one of the groups.
The presentation of the same model in the regression field, and specifically in R, considers an overall intercept, corresponding to one of the groups, and the model matrix could be presented as:
$$X=\begin{bmatrix} 
\color{red}\vdots & 0 & 0 &\color{red}\vdots  & 0 &0 &   0\\
\color{red}{J_{3n,1}} & 1_{n_2} & 0 & \color{red}{x}     & 0 & x_{n_2} & 0\\
\color{red}\vdots& 0 & 1_{n_3} & \color{red}\vdots & 0 & 0 &   x_{n_3}
\end{bmatrix}$$
of the OLS equation:
$$y =\color{red}{\beta_0} + \mu_i +\beta_1\, x_{n_1}+ \beta_2\,x_{n_2} \,+ \beta_3\,x_{n_3}\,+ \epsilon_i$$.
In this model, the overall intercept $\beta_0$ is modified at each group level by $\mu_i$, and the groups also have different slopes.
As you can see from the model matrices, the presentation belies the actual identity between regression and analysis of variance.
I like to kind of verify this with some lines of code and my favorite data set mtcars in R. I am using lm for ANCOVA according to Ben Bolker's paper available here.
mtcars$cyl <- as.factor(mtcars$cyl)         # Cylinders variable into factor w 3 levels
D <- mtcars  # The data set will be called D.
D <- D[order(D$cyl, decreasing = FALSE),]   # Ordering obs. for block matrices.

model.matrix(lm(mpg ~ wt * cyl, D))         # This is the model matrix for ANCOVA

As to the part of the question about what method to use (regression with R!) you may find amusing this on-line commentary I came across while writing this post.
A: The major advantage of linear regression is that it is robust to the violation of homogeneity of variance when sample sizes across groups are unequal. Another is that it facilitates the inclusion of several covariates (though this can also be easily accomplished through ANCOVA when you are interested in including just one covariate). Regression became widespread during the seventies in the advent of advances in computing power. You may also find regression more convenient if you are particularly interested in examining differences between particular levels of a categorical variable when there are more than two levels present (so long as you set up the dummy variable in the regression so that one of these two levels represents the reference group). This could save you the time of having to conduct post-hoc tests to compare the means between groups after running ANOVA. 
