# Is there a way to flip the hypotheses of the significance of variables in a logit regression model?

I am using the GLM Summary in R to determine the significance of the variables in the logistic regression model. I am trying to figure out if there is a way to flip the null and alternative hypotheses such that the null is that a certain variable is not insignificant in the logistic regression model and the alternative is that a certain variable is insignificant in the logistic regression model. This way, if I reject the null, I can accept the alternative with some level of significance. Is there a way to do this? Or is there some other test or model I can use?

No, this is not possible. To show if a parameter $\theta$ (in your case a population effect on the log-odds) is exactly 0 would need an infinite sample size.
You can perform a so-called equivalence test though. It works as follows: Before the analysis, you specify a range $R$ for the $\theta$ which you would judge as being "similar to 0", e.g. $R = [-0.1, 0.1]$ (depending on the meaning and scaling of the variable in question). Then you let the software compute a 90% confidence interval for $\theta$. If it is fully contained in $R$, then you can claim "similarity to 0" at a level of 95%. (It is somewhat unintuitive why a 90% c.i. is sufficient, but that's life.)