# Repeated measures paired test

I have the following data:
2 conditions - experimental & control.
20 participants, each having 3 results for each condition.

Like in the following table:

| Condition    | PARTICIPANT #1 | PARTICIPANT #2 | PARTICIPANT #3 |
|--------------|----------------|----------------|----------------|
| EXPERIMENTAL | Result #1      | Result #1      | Result #1      |
|              | Result #2      | Result #2      | Result #2      |
|              | Result #3      | Result #3      | Result #3      |
|--------------|----------------|----------------|----------------|
| CONTROL      | Result #1      | Result #1      | Result #1      |
|              | Result #2      | Result #2      | Result #2      |
|              | Result #3      | Result #3      | Result #3      |
|--------------|----------------|----------------|----------------|


I need to compare between the 2 conditions in a way that takes into account that the data is paired regarding participants (take into account that the same participant took part in both conditions).

It seems to me like the best approach is a 2 factor repeated measures anova, but i would like to be sure and to know if there are other ways to analyze this type of data.

Also, i would like to know which test should i choose if the order of the results (1-2-3) matters.

I'll appreciate any input! Thank you!

A repeated measures t test. It is essentially equivalent to a repeated measures ANOVA (where $t^2 = F$). Furthermore, both tests in the end just test the difference between the scores against zero (so you could also do this yourself, calculate the difference and test them with a one sample t test against zero).