alpha value in Bonferroni corrected test I ran some analysis with Bonferroni corrected test in R. Now my supervisor asked me what the value for alpha is for my analysis and since I haven't changed the default value it should be same as default. The problem is that I couldn't find any documentation or code that shows me the default value for alpha.
pairwise.t.test(ctData.df$SR, ctData.df$cond, p.adj = "bonf")

 A: tl;dr; pairwise.t.test doesn't set an alpha value, it gives adjusted p-values.
pairwise.t.test returns the adjusted p-values themselves, so it's up to you to decide on your own "alpha" (confidence level cutoff), if you're going to use the so-called Neyman-Pearson approach to dichotomize results into "reject null hypothesis" vs. "fail to reject null hypothesis".  Adapting the example from ?pairwise.t.test:
airquality$Month <- factor(airquality$Month, labels = month.abb[5:9])
with(airquality,pairwise.t.test(Ozone,Month,p.adj="bonf"))

Pairwise comparisons using t tests with pooled SD 

data:  Ozone and Month 

    May     Jun     Jul     Aug    
Jun 1.00000 -       -       -      
Jul 0.00029 0.10225 -       -      
Aug 0.00019 0.08312 1.00000 -      
Sep 1.00000 1.00000 0.00697 0.00485

P value adjustment method: bonferroni 

So you need to look at the values in the table (which are the Bonferroni-adjusted p-values) and decide on the basis of your own alpha. For example, for the comparison of August and June (adjusted p-value = 0.08312), you could decide to reject the null hypothesis if your alpha=0.1, or fail to reject it if your alpha=0.05 ...
Unsolicited PS: unless you absolutely must, there's no reason to use Bonferroni instead of the default Holm correction. ?p.adjust even says:

There seems no reason to use the
       unmodified Bonferroni correction because it is dominated by Holm's
       method, which is also valid under arbitrary assumptions.

