Which statistical test to perform for two identical experiments across different conditions I have two identical experiments. Each experiment have gene expressions for 12 samples with different conditions: untreated. Dosage 50. 100. 200.
I want to prove that these two experiments are identical or replicable. I can do two sample t tests across each conditions but will have 4 p-Values then. What’s a good test with a single p-value?

Stack the two datasets into one, and add a factor exper that codifies which experiment each observation is from. Fit a model with formula response - exper + Dosage. Then look at the anova test ofexper.
But really, if the objective is to show the experiments are the same, failing to find a significant difference does not accomplish that. Instead, you should do an equivalence test based on a defensible threshold for how big a difference in mean response would still be considered "the same." That entails using the two one-sided test (TOST) procedure for equivalence of exper means.
