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In simple, the Bonferroni correction factor is a strategy to make the alpha level more conservative in the light of multiple statistical tests that could derive a statistical outcome just from the testing itself...a spurious outcome...not due to any real difference in groups under comparison...it is a multiple-comparison adjustment when several dependent or independent statistical tests are being performed all at once, and it works by adjusting downwards the alpha, to account for the number of comparisons being performed. It raises the standard of proof or put another way, the evidence via the statistical test or the test statistic must be large enough to fall within the reduced alpha region of the curve...the so called 'extreme regions' that are normally set at 0.05...this is made smaller by dividing the alpha by the number of tests/comparisons done so that you result has to be stronger to achieve significance as the threshold has been moved so that the 'rejection region' under the curve is now smaller. Remember, alpha (0.05) is really the chance of making a Type I error (declaring significance when it does not exist) or saying there is a difference when there is none...so you are trying to reduce the chance of Type i error due to the multiple tests that raise the risk of a Type I error...remember the real aim of these tests is to uncover "is the difference I am seeing via the statistical test real and is it due to the differences in the groups under comparison and not due to chance alone...did the different treatments or interventions differ that much to result in a significant difference or was this difference due to random error"....the Bonferroni raises the threshold so to speak, so that you are making it harder to declare significance....so if doing post hoc analysis looking to see where differences lie (lets say after doing an ANOVA with a significant F test), the Bonferroni adjusts the alpha down so that the multiple comparisons you are doing between the difference treatment groups (likely you would be looking at at least 3 in ANOVA), do not yield significance between treatment arms just by spurious means.... Best, Paul E. Alexander ----- Original Message ---- From: Dr Ebtisam <[log in to unmask]> To: [log in to unmask] Sent: Thu, May 19, 2011 4:16:20 AM Subject: Bonferroni correction Hi all I am trying to put the meaning of Bonferroni correction tro an example and i cant, i know its applicable to multiple testing... could any one answer me kindly, and if possible tro an example to illustrate its application. Regards Dr Ebtisam