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§ Probability

Formal Probability Rules

§ Probability

Formal Probability Rules

CCSS.7.SP3 min read

Formal probability rules provide systematic methods for calculating the likelihood of combined events. The addition rule determines probabilities for 'A or B' scenarios, whilst the multiplication rule handles 'A and B' situations. These rules form the foundation for analysing complex probability problems in Year 9 and GCSE mathematics.

§ 01

Why it matters

Formal probability rules underpin risk assessment in insurance, where actuaries calculate premiums by combining multiple risk factors. Weather forecasters use these principles to determine the probability of rain occurring on consecutive days, applying independence assumptions. In medicine, diagnostic tests combine probabilities from multiple symptoms to assess disease likelihood. Quality control in manufacturing relies on these rules to calculate failure rates when multiple components must function together. Sports betting odds reflect sophisticated probability calculations using addition and multiplication rules. The complement rule appears in reliability engineering, where P(system failure) = 1 - P(system works). These concepts prepare students for A-level statistics and university-level probability theory, forming essential groundwork for careers in data science, finance, and research.

§ 02

How to solve formal probability rules

Probability — Addition & Multiplication Rules

  • Addition rule (OR): P(A or B) = P(A) + P(B) − P(A and B).
  • If mutually exclusive: P(A or B) = P(A) + P(B).
  • Multiplication rule (AND, independent): P(A and B) = P(A) × P(B).
  • Use tree diagrams to organise compound events.

Example: Two coins: P(HH) = 12 × 12 = 14.

§ 03

Worked examples

Beginner§ 01

P(A) = 0.75. Find P(not A).

Answer: 0.25

  1. Apply complement rule P(not A) = 1 - P(A) = 1 - 0.75 = 0.25 The complement rule: P(not A) = 1 - P(A).
Easy§ 02

P(A) = 16, P(B) = 14, A and B are mutually exclusive. P(A or B)?

Answer: 512

  1. Apply addition rule for mutually exclusive events P(A or B) = P(A) + P(B) = 16 + 14 When events cannot happen together, add their probabilities.
  2. Calculate 16 + 14 = 512 Find a common denominator and add.
Medium§ 03

P(rain) = 0.5 each day. P(no rain both days) if independent?

Answer: 0.25

  1. Find P(no rain) for one day P(no rain) = 1 - 0.5 = 0.5 Use the complement rule.
  2. Multiply for independent events P(no rain both) = 0.5 x 0.5 = 0.25 For independent events, multiply the individual probabilities.
§ 04

Common mistakes

  • Adding probabilities incorrectly when events overlap, such as calculating P(A or B) = 0.4 + 0.3 = 0.7 instead of applying the general addition rule P(A or B) = 0.4 + 0.3 - 0.1 = 0.6 when P(A and B) = 0.1.
  • Multiplying dependent events as if independent, computing P(second card is heart | first was heart) × P(first is heart) = 12/51 × 13/52 instead of recognising the conditional nature of card drawing without replacement.
  • Forgetting the complement relationship, writing P(not A) = 0.3 when P(A) = 0.6 instead of correctly calculating P(not A) = 1 - 0.6 = 0.4 using the complement rule.
§ 05

Frequently asked questions

What is the difference between mutually exclusive and independent events?
Mutually exclusive events cannot occur simultaneously (rolling a 3 and 4 on one die), whilst independent events have no influence on each other (two separate coin flips). Mutually exclusive events use P(A or B) = P(A) + P(B), but independent events use P(A and B) = P(A) × P(B).
When do you use the general addition rule versus the simple addition rule?
Use the general addition rule P(A or B) = P(A) + P(B) - P(A and B) when events can overlap. Use the simple rule P(A or B) = P(A) + P(B) only for mutually exclusive events where P(A and B) = 0, such as drawing a heart or spade from one card.
How do tree diagrams help with probability calculations?
Tree diagrams organise sequential events visually, showing all possible outcomes and their probabilities. Each branch represents a choice, with probabilities multiplied along paths and added across different paths. This prevents counting errors and ensures systematic coverage of all possibilities in compound probability problems.
Why does P(A) + P(not A) always equal 1?
The sample space contains all possible outcomes, so either event A occurs or it doesn't occur. Since these are the only two possibilities and they're mutually exclusive, their probabilities must sum to 1. This fundamental principle underlies the complement rule P(not A) = 1 - P(A).
Can probabilities be added and multiplied in the same problem?
Yes, complex problems often require both operations. For instance, finding P(at least one success in three trials) uses P(success) + P(success) + P(success) - overlaps, combining addition for 'or' situations with multiplication for calculating individual compound events like P(success on trials 1 and 2).
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See also

§ 06

Where to next?

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