title: Multiple testing methods
body: <p>I want to simulate the effect of different kinds of multiple testing correction such as <code>Bonferroni</code>, <code>Fisher's LSD</code>, <code>DUncan</code>, <code>Dunn-Sidak Newman-Keuls</code>, <code>Tukey</code>, etc... on <code>Anova</code>.</p>
  
  <p>I guess I should simply run a regular <code>Anova</code>. And then accept as significant <code>p.value</code>s which I calculate by using <code>p.adjust</code>. But I'm not getting how this <code>p.adjust</code> function works. Could give me some insights about <code>p.adjust()</code> ?</p>
  
  <p>when running:</p>
  
  <pre><code>&gt; p.adjust(c(0.05,0.05,0.1),"bonferroni")
  # [1] 0.15 0.15 0.30
  </code></pre>
  
  <p>Could someone explain as to what does this mean? </p>
  
  <p>Thank you for your answer. I kinda know a bit of all that. But I still don't understand the output of p.adjust. I'd expect that...</p>
  
  <blockquote>
    <p>P.adjust(0.08,'bonferroni',n=10)</p>
  </blockquote>
  
  <p>... would returns 0.008 and not 0.8. n=10 doesn't it mean that I'm doing 10 comparisons. and isn't 0.08 the "original alpha" (I mean the threshold I'd use to reject the NULL hypothesis if I had one simple comparison) </p>