Probability: Vocabulary Set

Probability: Vocabulary Set

Learn course language through meaning, representation, example, near-miss, and connection instead of definition-only memorization. Studies random variables, distributions, expectation, conditional probability, and stochastic reasoning. Course titles, local sequences, grading, safety rules, required tools, and assessment formats vary; align this resource with the current syllabus and instructor directions.

Learning outcomes

• Explain and apply sample spaces, then connect it to conditional probability using course-appropriate evidence.
• Explain and apply conditional probability, then connect it to discrete random variables using course-appropriate evidence.
• Explain and apply discrete random variables, then connect it to continuous distributions using course-appropriate evidence.
• Explain and apply continuous distributions, then connect it to expectation and variance using course-appropriate evidence.
• Explain and apply expectation and variance, then connect it to sample spaces using course-appropriate evidence.

Prerequisite readiness

algebra, functions, summation, basic calculus for continuous topics, and set notation Use a short ungraded check, repair the smallest missing skill, and immediately retest it in a course-level task.

Core vocabulary network

• sample spaces: define in course language, illustrate, contrast with conditional probability, and use in a claim supported by evidence.
• conditional probability: define in course language, illustrate, contrast with discrete random variables, and use in a claim supported by evidence.
• discrete random variables: define in course language, illustrate, contrast with continuous distributions, and use in a claim supported by evidence.
• continuous distributions: define in course language, illustrate, contrast with expectation and variance, and use in a claim supported by evidence.
• expectation and variance: define in course language, illustrate, contrast with sample spaces, and use in a claim supported by evidence.

Retrieval check

Sort examples and near-misses without labels, then explain the decisive feature before checking notes.

Reliable method

define the experiment, specify events or variables, choose a rule or distribution, calculate, and test limiting cases Keep assumptions, intermediate reasoning, units, sources, tool use, and checks visible so another learner can follow the decision process.

Representative application

Use Bayes’ rule to update a probability and explain why base rates matter to interpretation. Predict a reasonable result before working, compare the outcome with the prediction, and explain limitations or alternative interpretations.

Error recovery

Watch for confusing mutually exclusive with independent events or ignoring conditioning and base rates. Mark the first unsupported move, classify the cause, correct the reasoning, and schedule a fresh mixed recheck after a delay.

Accessibility, integrity, and safety

Use approved accommodations and accessible formats. Follow course rules for collaboration, citation, calculators, software, generative tools, laboratories, clinical settings, field activity, privacy, copyright, and human or animal subjects. Never use Exams.fit to obtain protected questions or bypass assessment rules.

Evidence to save

When permitted, preserve a probability model with assumptions, derivation, simulation check, and interpretation with the prompt, first attempt, feedback, revision, verification, and reflection. Remove restricted assessment content and private or proprietary information.

Open-learning reference

Compare this original Exams.fit resource with the relevant OpenStax collection and MIT OpenCourseWare when they match the local course. Reviewed August 2, 2026. This page does not replace the current syllabus, instructor, institution, or qualified professional.