Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 3m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample1h 1m
- 10. Hypothesis Testing for Two Samples2h 8m
- 11. Correlation48m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
4. Probability
Addition Rule
Problem 3.3.4
Textbook Question
True or False? In Exercises 3-6, determine whether the statement is true or false. If it is false,
explain why.
4. When two events are independent, they are also mutually exclusive.

1
Step 1: Begin by understanding the definitions of 'independent events' and 'mutually exclusive events'. Independent events are those where the occurrence of one event does not affect the probability of the other event occurring. Mathematically, this is expressed as P(A ∩ B) = P(A) * P(B). Mutually exclusive events, on the other hand, are events that cannot occur at the same time. Mathematically, this means P(A ∩ B) = 0.
Step 2: Analyze the relationship between these two concepts. If two events are mutually exclusive, the probability of their intersection (both occurring simultaneously) is zero. However, for independent events, the probability of their intersection is generally non-zero unless one of the events has a probability of zero.
Step 3: Consider an example to clarify the distinction. Suppose Event A is 'rolling a 3 on a die' and Event B is 'rolling a 4 on the same die'. These events are mutually exclusive because you cannot roll both a 3 and a 4 in a single roll. However, they are not independent because the occurrence of one event does not affect the occurrence of the other—they are mutually exclusive instead.
Step 4: Reflect on whether independence and mutual exclusivity can coexist. If two events are mutually exclusive, the occurrence of one event means the other cannot occur, which violates the definition of independence. Therefore, mutually exclusive events cannot be independent.
Step 5: Conclude that the statement 'When two events are independent, they are also mutually exclusive' is false. Provide reasoning that independence and mutual exclusivity are distinct concepts and cannot occur simultaneously.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Independent Events
Independent events are those whose occurrence or non-occurrence does not affect the probability of the other event occurring. For example, flipping a coin and rolling a die are independent events because the outcome of one does not influence the outcome of the other.
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Mutually Exclusive Events
Mutually exclusive events are events that cannot occur at the same time. For instance, when flipping a coin, the outcomes 'heads' and 'tails' are mutually exclusive because if one occurs, the other cannot. This concept is crucial in probability as it affects how we calculate the likelihood of combined events.
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Probability of Mutually Exclusive Events
Relationship Between Independence and Mutual Exclusivity
The relationship between independent and mutually exclusive events is that they are fundamentally different. While independent events can occur simultaneously without affecting each other's probabilities, mutually exclusive events cannot occur together at all. Therefore, if two events are independent, they cannot be mutually exclusive.
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Probability of Mutually Exclusive Events
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