have tested varying concentrations of DrugA, and DrugB and measured the effect on cell death. I do this process in quadruplicate (4 replicates) and record the mean value of % Cell Death.
Sometimes, I see that a very specific concentration pair causes the effect is much more than expected - like below I made up a similar example where the %Cell Death is 90 when when DrugA=2 and DrugB=3.
How can I determine that this result of 90% cell death is statistically significant, and obtain a p-value?
In experimental design, selecting the null hypothesis, H_0 is a critical first step. For that, a statistical test appropriate to the data is needed. Here, it is unclear what the nature of that data is. If, for example, each drug pair were tested once and the response variable Y is the percentage cell mortality, what is there to test? That 100% of one test showed 90% mortality? No statistics are involved in that case; it's just one observation.
On the other hand, if there were 10,000 observations and the mean cell death was 90%, the probability can be tested.
technocrat makes some very good points though as an old but budding Baysian NHST ( null hypothesis significance testing) may not be the way to go . In any case I think you need statistical advice rather than R advice.
Does your organisation have a consulting statistician? If not readers here or, perhaps even better, on CrossValidated would need a fairly detailed description of what you are doing and why, expressed in subject matter terms.
. In any case I think you need statistical advice rather than R advice.