Math // Probability

Complementary events

Understand and apply the rule that probabilities of all mutually exclusive outcomes sum to one; use this to find the probability of a complementary event (P(not A) = 1 − P(A))

Ages
11 to 13
Type
Concept
Builds on
2 topics
Leads to
1 topic
Standards
3 codes

Quick check

If a spinner has three sections and one has a probability of 0.4 and another has 0.35, can the learner work out the probability of landing on the third section without being told?

Ask this to see whether the topic is secure.

Topic // Mastery

What mastery looks like

What a learner can do once this topic is secure.
  • Verify that probabilities of all outcomes listed for a spinner sum to 1
  • Calculate the probability of NOT rolling a 6 as 1 − 1/6 = 5/6
  • Identify an error in a probability distribution where the values do not sum to 1

Topic // Map

Where this fits

What to learn first and what this topic opens up, with the reason for each link. Required links come first.
  • RequiredMust come first
  • HelpfulHelps, but not essential

Before this

2 topics
  • Probability // Ages 10 to 11

    The formal mutually-exclusive outcomes rule extends the simpler complement rule introduced at age 10-11

  • Probability // Ages 11 to 13

    Probabilities summing to 1 builds directly on understanding the probability scale

You are here

Complementary events

Ages 11 to 13 // Concept

Leads to

1 topic
  • Probability // Ages 12 to 14

    Enumerating sets for probability requires understanding that all outcomes sum to 1

Topic // Research-informed

Teaching notes

From the Marble Skill Taxonomy. These notes have not been validated in classrooms, so treat them as a starting point for your own judgement.

The approach

Teach complementary events as two linked ideas: the probabilities of a complete set of mutually exclusive outcomes sum to 1, and P(not A) = 1 − P(A). The distinctive crux is the condition MUTUALLY EXCLUSIVE — adding probabilities only works when outcomes cannot occur together. Make the overlap trap explicit: even/prime on a die share the number 2, so adding 1/2 + 1/2 reaches exactly 1 and falsely suggests 'certain', when the true probability is only 5/6. Practise finding complements efficiently and rejecting distributions that do not total 1.

Common misconceptions

  • Adding probabilities of events that are NOT mutually exclusive (overlapping events like even/prime) as though they were
  • Computing P(not A) by something other than 1 − P(A), or thinking P(not A) = P(A)
  • Assuming any list of events sums to 1 even when it is not exhaustive
  • Adding fractions without a common denominator when combining or complementing

Useful representations

  • Unit bar partitioned into non-overlapping outcome segments
  • Complete probability distribution table totalling 1
  • Two-circle diagram showing overlap vs no overlap (mutually exclusive or not)
  • P(not A) as the whole minus the A-segment

Teaching practices

  • Define mutually exclusive precisely (cannot happen at the same time) and test each situation for overlap before adding
  • Teach P(not A) = 1 − P(A) as the efficient route, contrasted with counting every unfavourable outcome
  • Use a complete outcome table / unit bar so 'exhaustive and mutually exclusive' is visible as segments filling the whole with no overlap
  • Expose the overlapping trap deliberately: ask for P(even or prime) on a die and show why 1/2 + 1/2 is wrong
  • Practise spotting invalid distributions whose probabilities don't sum to 1

Activities to try

  1. 1

    Unit bar: complement and the overlap trap

    Why it works: Non-overlapping segments of a complete outcome set fill the bar to exactly 1, and P(not A) is simply the rest. Adding probabilities only works when the events share no outcome; overlapping events double-count the shared outcome, so the sum overstates the true probability — here it reaches exactly 1 while the true value is only 5/6.

    1. Use a bar representing probability 1, divided into the six equally likely outcomes of a fair die (each 1/6).
    2. Choose an event, e.g. 'rolling a 6'. Shade that segment, then see that P(not 6) is the whole bar minus that one segment: 1 − 1/6 = 5/6. Confirm this matches the five remaining faces without counting them one by one.
    3. Switch to overlap mode and select BOTH 'even' (2, 4, 6) and 'prime' (2, 3, 5). Shade each region on the bar.
    4. Add P(even) = 1/2 and P(prime) = 1/2 to get 1, then compare with the bar: the arithmetic sum of the two lengths reaches the whole bar (suggesting 'certain'), yet the actual shaded union only covers five of the six segments (5/6). The extra 1/6 is the number 2, which sits in both regions and has been counted twice.
    5. List the distinct outcomes that are even OR prime: 2, 3, 4, 5, 6 — five faces — so P(even or prime) = 5/6. Note that 1 is neither, so the event is NOT certain.
  2. 2

    Predict, roll, explain: is 'even or prime' certain?

    Why it works: An outcome (1) that is neither even nor prime disproves the 'certain' claim and exposes that adding overlapping probabilities over-counts the shared outcome. Checking for a shared outcome BEFORE adding is the deciding skill.

    1. Before rolling: 'On a fair die, is rolling a number that is even OR prime certain? Predict yes or no, give a probability, and explain your reasoning.'
    2. Record the prediction (a learner who adds 1/2 + 1/2 will predict 'certain, probability 1').
    3. Roll the die several times. A 1 is likely to appear but is not guaranteed in a small number of rolls (each roll has a 1/6 chance). If a 1 appears, note that it is neither even nor prime, which contradicts a 'certain' prediction. If no 1 appears, check the faces anyway: 1 is a possible outcome that is neither even nor prime, so the event still cannot be certain. Not rolling a 1 does not show the event is certain.
    4. Revisit the reasoning: list the even-or-prime outcomes, find the double-counted 2, and correct the probability to 5/6.
    5. Discuss: when is it safe to add two probabilities, and when is it not?
  3. 3

    Sort and check: mutually exclusive or overlapping, and valid distributions

    Why it works: Testing for overlap is a prerequisite to adding; sum-to-1 only applies to a complete (exhaustive) set of mutually exclusive outcomes; complements are found by subtracting from 1.

    1. Sort pairs of events into 'mutually exclusive' (cannot both happen) or 'overlapping' (share at least one outcome). Include die examples such as {even}/{prime} (overlap at 2), {1,2,3}/{4,5,6} (no overlap), {multiple of 3}/{even} (overlap at 6).
    2. For each overlapping pair, list the shared outcome(s) explicitly.
    3. Fill in complements directly: P(not 6) = 1 − 1/6 = 5/6; P(not even) = 1 − 1/2 = 1/2.
    4. Examine several probability distributions and decide which are invalid because the probabilities do not sum to 1, or because the list is not exhaustive. Explain each decision.
    5. Verify that the probabilities listed for a given spinner sum to 1, and find any missing probability by subtracting from 1.

Topic // Standards

Curriculum standards

Codes for the curriculum statements this topic maps to. Each publisher owns its standard text.
Common Core State Standards for Mathematics
7.SP.57.SP.7a
The national curriculum in England: Key stages 1 and 2 framework document
KS3.Maths.Prob.2

Math // Probability