# If the f-test is insignificant but coefficients are significant, can I use it?

If the linear regression's f-test is insignificant but its coefficients are significant in t-test, can I use this regression and its coefficients?

In academic journals, I find people use linear regression without reporting f-test values but use its coefficients as long as they are significant.

Is it ok to use the linear regression's coefficients ignoring its f-test values?

• The possible causes of such a situation are extensively discussed at stats.stackexchange.com/questions/24720. But if the F-test tells you the fit is insignificant, then you have no justification to look any further: doing so could validly be characterized as "p-hacking" or "data snooping."
– whuber
Commented Jun 29, 2017 at 14:52
• So your short answer to the question is "No, you cannot use the coefficients although they are significant if the regression f-test is insignificant".
– Eric
Commented Jun 29, 2017 at 15:00
• If you need a short answer: No. Commented Jun 29, 2017 at 15:01
• While this sequential testing procedure is used sometimes, there are situations where it seems quite rubbish. E.g. when confirming the result of a two-sample comparison by running a linear model with potential confounders. It all depends on the goal of your analysis. Commented Jun 29, 2017 at 15:02
• Then if the f-test is insignificant but coefficients are significant, I understand I cannot use these coefficients. But if I still need to report this regression, is it normal to show regression with insignificant f-test result but with significant coefficients although I do not interpret them at all?
– Eric
Commented Jun 29, 2017 at 15:15

F test is the first thing to do in regression after identifying the features and if it fails then it means that none of the predictors/features for the given degrees of freedom is significant, so stop there. Now, I get this question a lot that if we have any single significant predictor present in the model then why bother an $$F$$ test. The answer here is that suppose we have $$100$$ predictors and we do not do an $$F$$ test and just rely on $$t$$ test to see if any predictor is significant using p-value $$\le 0.05$$ and then proceed with those which are significant, then there is a serious problem with this approach which is, that by chance we will have $$5$$ out of $$100$$ predictors which won't be significant but will have low p value due to chance alone. This is the reason an $$F$$ test is important and the first criteria to fit a model.