We generated Monte Carlo simulations and binomial probability distributions for different MCQ test structures, varying in number of questions, choices per question, and pass mark. Educated guessing and student blunder (incorrect response despite knowing the material) were modeled. Knowledge levels associated with α<5% were determined. Pass levels were then recalculated for each test to ensure 50% and 60% levels of student knowledge, at a <5% false-positive rate.
Pass marks designed to detect failing levels of knowledge at <5% error rate varied strongly with MCQ test structure. Test specificity increased with test length, number of choices per question, and pass mark, revealing that pass marks should be customized to individual test structure. A discussion on appropriate MCQ structure and content is presented.
Pass marks need to address MCQ test structure and the probabilistic nature of MCQ testing to accurately discriminate learner competency.