I have measured 8 different features on the same mice on three different days. I want to compare the means of those 3 days based on the features.

I have executed paired T-tests for each feature in the following way: Day1Feature1 compared to Day2Feature1, Day1Feature2 compared to Day2Feature2, etc. Therefore, as far as I know, after I compare all of the combinations I can see the differences between the days.

On the other hand, one of my colleague suggested that I should use Linear Mixed Models in a case like this.

Which approach would be better in this case, and why?