# sjstats- Model is overfitting zero-counts

I have a glmmTMB model and I used the overdisp and the zero_count functions with these results:

overdisp(glmmtmb_poisson)

# Overdispersion test
#
# dispersion ratio = 0.2953
# Pearson's Chi-Squared = 3104.5094
# p-value = 1.0000
#
# No overdispersion detected.

zero_count(glmmtmb_poisson)
# Observed zero-counts: 9946
# Predicted zero-counts: 9953
# Ratio: 1.00
#
# Model is overfitting zero-counts


I am not sure what this means. I understand that the model is not overdispersed nor zero-inflated, but I am not sure what this overfitting means. Also, there was a notes saying that in the case of glmm a p value larger than 0.05 indicates overdispersion, yet the p value does not seem to indicate overdispersion in my case.

Should I just go ahead and run a simple glmmTMB model as my final model without any further worry about zero inflation and overdispersion?

Thank you very much!