The right statistical test when you have a before/after + trial and control group I am trying to understand what statistical test is best in circumstances where you have both a before/after (so paired data) and a control group
There are t-tests available for one of each scenario (two independent samples t-test and paired t test) but I cannot find any information on what to do when you have a mix of the two.
Any help is appreciated.

Assuming you have before/after data for the control group as well, you are describing a BACI (Before-After-Control-Impact) design.
In this case, the best approach is probably to fit a linear model (ANOVA) with main effects for time (before/after) and treatment status (control/treatment), an an interaction term between the two. The interaction term will be most important in evaluating the results of the fit, and I strongly recommend plotting the model output to understand the results well (interpreting interaction terms from the coefficients alone is a common source of confusion, and it is needless).
In R, the code for the model would look like this:
lm(outcome ~ time * treatment_status, data = dat)

Other assumptions I have made:

*

*You have a single measurement before and after (if you have multiple measurements before and after, you might simply need to add random effect to address this).

*You have multiple individuals (or sites, or whatever the experimental unit is) in both control and treatment groups.

