Guideline one using which statistical test? Are there any guide lines on determining which test to use?
For example, given 100 subjects with both Exam A and Exam B, and some student observations for those exams, i want to compare which exam is harder.
More specifically, I have 100 different subjects (ex. math, french, english, ...etc), and each subjects consists 2 exams, exam A & exam B. Now, I distribute these exams to a class of 100 and if a student score above 50%, then the observation is set as 1 and 0 otherwise.
An example table:
Subject Exam    Class Score
 MATH    A           90
 MATH    B           10
FRENCH   A           51
FRENCH   B           49
...      ...        ...

Which statistical test should i perform if I want to compare which exam is harder?
Summary of the question (by @user2974951):

*

*We have 100 students, each student takes test A and B for 100 different subjects. The results of the tests are independent, so the results of test A does not affect the result of test B, and a student score in subject 1 will have no effect on the score of subject 2. The data is already aggregated on the subject and exam level, that is we only have the frequency of students which passed. Our goal is to determine whether test A is harder than test B, based on the aggregated data.

 A: Given the assumption that all observations are independent, that is within the subjects and between the subjects, then a simple t-test can be used to solve this - assuming all the assumptions of the t-test are valid (your data is not really continuous so they might not hold, but it might be a good approximation). Otherwise an equivalent non-parametric test might work better.
Your goal is to determine whether test A is harder than test B, or to put it differently if the frequency of test A is higher than that of test B. Given that you only have aggregated data, the results are interpreted on the frequencies of students which passed, rather than say the average score.
So for ex. you might find that the expected frequency for test A is 67, while the expected frequency for test B is 54. If the standard error is small enough you would find that the difference is statistically significant.
If, however, there is a relation between the exams, then you can use a paired t-test. If there is a relation within the students, then a linear mixed model can be used.
