Which t-test to use for a two-group pre- post-test design? I have data on 26 participants (13 from computing and remaining 13 from non computing) who have participated in my research. Each participant is treated with a lab module (Hands on Robotics Session). Now each participant will be evaluated using a rubric on scale of 1 to 4. This experiment has both pre and post test.
For my research i want to evaluate the following questions:
Research question 1


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*Null Hypothesis: students do not learn about computational thinking (programming basics and algorithmic thinking) with the help of robotics.

*Alternate Hypothesis: Students learn about computational thinking (programming basics and algorithmic thinking) with the help of robotics.


To evaluate the above question, the categories i will be considering are Plan, Implementation and Knowledge gained on a scale of Excellent, Good, Fair and Poor. 
I think i should use DEPENDENT T-TEST FOR PAIRED SAMPLES. Am i correct?
Research question 2:


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*Null Hypothesis: Participants background (computing and non computing) has no effect in learning about algorithmic thinking with help of robotics

*Alternate Hypothesis:Participants background (computing and non computing) has an effect in learning about algorithmic thinking with help of robotics 


I will also evaluate question 2 on a scale of Excellent, Good, Fair and Poor, but with respect to background.
My Statistical Question
Which T test should I use for each of my research questions?
 A: Since you are interested in measuring an increase pre- and post-test, it seems to me that you should use a paired test.
An issue here is that your variables are likely non Gaussian since they are among 4 categories. If your sample size is big (very roughly larger than 50), then it is no big deal. Otherwise, I would use the Wilcoxon signed rank test which is a non parametric analog of the paired t-test.
A: gui11aume beat me to it.  He said exactly what I had in mind when I read the question.  Since scaled measurements are not going to fit a normal error distribution very well a nonparametric paired test is the best way to go in my view and for that I would have recommended the Wilcoxon signed rank test.  On the other hand if you were comparing scores by domains where many responses are summed, normality is not a bad assumption and the t test is fairly robust anyway.  So in that situation a paired t test might be okay.
A: I think you are having problems with the nature and elaboration of your alternative hipotesis. Your investigation/reserach hypotesis (and all the theoretical model associated with it) is your alternative hipothesis.
Since the variable is clearly ordinal, i´d prefer a Wilcoxon rank test. But anyway i suggest to work out a little bit more your hipothesis.
