I have run a multiple linear regression using stepwise regression to select the best model, however the best model returned has a non-significant variable. When I remove this the AIC value goes up indicating the model without the significant variable is a worse fit. Should I remove the non-significant predictor or should I leave it in as it is a better model?

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    $\begingroup$ What is your goal here? Prediction or explanation? How big is your data? $\endgroup$ Jan 18, 2015 at 15:09
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    $\begingroup$ Thanks for your answer below. My goal is prediction and my data set has 590 cases. $\endgroup$
    – Poppy
    Jan 18, 2015 at 16:44
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    $\begingroup$ I give a detailed list of the problems with stepwise model building in my answer here: stats.stackexchange.com/questions/115843/…. $\endgroup$
    – Alexis
    Jan 19, 2015 at 1:01
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    $\begingroup$ Suppressor variables are often not significant, yet they can affect fit-statistics a lot. Might be interesting to check for suppressor effects (especially with a view to model interpretation). $\endgroup$ Mar 15, 2017 at 16:35

3 Answers 3


Leave it in. The data are incapable of really telling you which model is "better" unless you use AIC in a highly structured way (e.g. on a pre-specified large group of variables), and removing insignificant variables invalidates the estimate of $\sigma^2$ and all $P$-values, standard errors, and confidence limits in addition to invalidating the formula for adjusted $R^2$. Much is written about these issues on this site.


NB: A corollary to Frank Harrell's answer is that stepwise variable selection should not be used in the first place. That is, not only is it a mistake to discard that final 'leftover' non-significant covariate, but it was even more wrong to employ an automated procedure (stepwise variable selection) designed to produce a cascade of many such mistakes very quickly in an interdependent and irreproducible fashion.


You need to test your model on multiple test datasets. AIC is measure of model fitting not accuracy. Please read section 3.3 ( Subset Selection) in this book -


removing variables is suggested because of 2 reasons-

The first is prediction accuracy: keeping all variables often have low bias but large variance. Prediction accuracy can sometimes be improved by shrinking or setting some coefficients to zero. By doing so we sacrifice a little bit of bias to reduce the variance of the predicted values, and hence may improve the overall prediction accuracy.

The second reason is interpretation. With a large number of predictors, we often would like to determine a smaller subset that exhibit the strongest effects. In order to get the “big picture,” we are willing to sacrifice some of the small details.


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