3.2 Independent and Mutually Exclusive Events

Independent and mutually exclusive events describe different relationships between outcomes. Independent events do not affect each other's probabilities, while mutually exclusive events cannot happen at the same time in one trial.

The probability rules change depending on which relationship applies. For independent events, you multiply probabilities; for mutually exclusive events, you add probabilities because there is no overlap.

Independent and Mutually Exclusive Events

Independence vs mutual exclusivity

• Independent events occur when the outcome of one event does not influence the probability of another event happening (coin flips)
  • Probability of event A given event B has occurred is equal to the probability of event A: $P(A|B) = P(A)$
  • Probability of event B given event A has occurred is equal to the probability of event B: $P(B|A) = P(B)$
• Mutually exclusive events cannot happen simultaneously in the same trial (rolling even or odd numbers on a die)
  • If one mutually exclusive event occurs, the other event(s) cannot occur in that trial
  • Probability of the intersection of mutually exclusive events A and B is zero: $P(A \cap B) = 0$
  • Venn diagrams can be used to visualize mutually exclusive events as non-overlapping circles

Probability calculations for event types

• For independent events, calculate the probability by multiplying the individual probabilities of each event
  • Probability of the intersection of independent events A and B: $P(A \cap B) = P(A) \times P(B)$
  • Probability of getting heads twice when flipping a fair coin: $0.5 \times 0.5 = 0.25$
• For mutually exclusive events, calculate the probability by adding the individual probabilities of each event
  • Probability of the union of mutually exclusive events A and B: $P(A \cup B) = P(A) + P(B)$
  • Probability of rolling an even or odd number on a fair six-sided die: $0.5 + 0.5 = 1$

Sampling methods and event dependence

1. Simple random sampling gives each item in the population an equal chance of being selected

   • Selections are independent of each other (drawing names from a hat with replacement)

2. Systematic sampling selects items at regular intervals from a list

   • Selections may be dependent on the ordering of the list (choosing every 10th person from an alphabetical list)

3. Stratified sampling divides the population into subgroups (strata) based on a characteristic

   • Simple random sampling is performed within each stratum (dividing population by age groups and randomly sampling within each group)
   • Selections within each stratum are independent, but the overall sample may be dependent on the chosen strata

4. Cluster sampling divides the population into naturally occurring groups (clusters)

   • A random sample of clusters is selected, and all items within the selected clusters are included (randomly selecting city blocks and surveying all households within those blocks)
   • Selections within clusters are dependent on each other

Additional Probability Concepts

• Set theory provides a foundation for understanding probability, including operations like union and intersection
• The law of total probability allows for calculating probabilities by considering all possible outcomes
• The complement rule states that the probability of an event not occurring is 1 minus the probability of it occurring