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I am running a generalised linear model in R. I have a single response variable and a maximum of 4 possible explanatory variables. I am adding each explanatory variable to the model sequentially, based on whether the coefficient is statistically significant.

If the coefficient for an explanatory variable is statistically significant at 0.05, the explanatory variable remains in the model. If the coefficient for an explanatory variable is NOT statistically significant at 0.05, the explanatory variable is removed from the model.

I am wondering if instead of using 0.05, I should be using a Bonferroni corrected P value? Should I use a threshold of 0.05/4 = 0.0125?

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  • $\begingroup$ What is the goal of your analysis and why are you doing forward selection? $\endgroup$ – Macro Jul 10 '12 at 18:01
  • $\begingroup$ If you are doing multiple testing and want to control the overall (so-called fmailywise significance level) you should adjust. Bonferroni is one conservative way but is not the only way that you can make the adjustment. If you are just using an informal procedure for deciding when to remove a variable from the model then you do not. But oyu don't have to use 0.05 either. people sometimes use 0.1 or 0.2. $\endgroup$ – Michael Chernick Jul 10 '12 at 19:10
  • $\begingroup$ Related question: Is adjusting p-values in a multiple regression for multiple comparisons a good idea?. See this thread to get a feel of why stepwise variable selection might not necessarily be a good idea. $\endgroup$ – chl Jul 10 '12 at 20:21

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