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Class 9 · Mathematics

Chapter 7: The Mathematics of Maybe: Introduction to Probability

Revision notes, worked problems, a question bank, assertion and reason questions and answers to the NCERT exercises. Tap a group to open it; opening one closes the others.

7.1What is probability?Randomness and the probability scale

Random experiments

  • A random experiment can be repeated; you know the possible outcomes but not which one will occur (tossing a coin, rolling a die, a lucky draw).
  • Probability measures how likely an event is. Subjective probability depends on opinion; we want objective estimates.
  • A coin toss decides who bats first because the outcome is unpredictable and each team has an equal chance (fair).

The probability scale

0¼½¾1impossibleless likelyeven chancemore likelycertain
The probability scale: 0 means impossible, 1 means certain; probability ½ is an even chance.
  • Probability P(E) lies from 0 (impossible) to 1 (certain): 0 ≤ P(E) ≤ 1.
  • Examples: a number greater than 6 on a die (0); rolling a 3 (1/6, less likely); heads on a coin (1/2, even chance); a number from 2 to 10 in a pack of 52 (36/52, more likely); a red sweet from a bag of red sweets (1).
7.2Measuring probability objectivelyExperimental and theoretical probability

Experimental probability

  • Experimental probability = number of times the event occurred ÷ total number of trials (also called the relative frequency).
  • Example: a 4 appears 8 times in 50 rolls: 8/50 = 0.16.
  • From statistical data of a sample: if 20 of 50 students like mango, P(mango) = 0.4; for 1500 students expect about 600. A larger and more representative sample gives a better estimate.

Theoretical probability

  • If all outcomes are equally likely: P(E) = number of favourable outcomes ÷ number of possible outcomes.
  • P(rolling a 4) = 1/6. P(letter B from PROBABILITY) = 2/11.
  • Law of Large Numbers: as the number of trials increases, experimental probability tends to get closer to theoretical probability.

Gambler’s fallacy

  • A fair coin or die has no memory. After six heads the chance of tails next is still 1/2; after three 6s the chance of another 6 is still 1/6.
  • Each toss or roll is independent of the earlier ones. Probability describes the long run, not the next trial.
7.3Sample spaces and eventsListing outcomes and choosing events

Sample space

  • The sample space S is the set of all possible outcomes; each outcome is listed exactly once. The number of elements is n(S).
  • Coin: S = {H, T}, n(S) = 2. Die: S = {1, 2, 3, 4, 5, 6}. Two coins: S = {HH, HT, TH, TT}, n(S) = 4.
  • The sample space must be detailed enough for the question (rain / no rain, or drizzle / light / heavy).

Events

  • An event is a subset of the sample space.
  • Two coins, at least one head: E = {HH, HT, TH}. Die, greater than 4: E = {5, 6}.
  • An event with no outcomes is impossible (P = 0); S itself is certain (P = 1).
  • Two dice: n(S) = 36. Sum 7 can occur in 6 ways, so P(sum 7) = 6/36 = 1/6.

Geometric probability

12345678
Fig. 7.7: 8 equal sectors, so each number has probability 1/8.
3 m2 mdiameter 1 m
Fig. 7.8: P(inside circle) = area of circle ÷ area of rectangle = (π/4)/6 = π/24.
  • For a spinner with 8 equal sectors, P(a particular number) = 1/8.
  • For a dart landing at random, P = favourable area ÷ total area. In Fig. 7.8: π/24 ≈ 0.131.
7.4Tree diagramsMulti-step experiments

With replacement

½½HH ¼HT ¼TH ¼TT ¼HTHT
Tree diagram for two tosses: 4 outcomes, each with probability ½ × ½ = ¼.
  • Each path from the start to the end is one outcome of the multi-step experiment. Multiply the probabilities along a path.
  • Two coins: 4 outcomes, each ½ × ½ = ¼. P(one head and one tail) = P(HT) + P(TH) = ½.

Without replacement

R 4/9B 5/9R 3/8B 5/8R 4/8B 4/8
Without replacement: after a red ball the basket has 3 red and 5 blue; after a blue, 4 red and 4 blue.
  • Basket with 4 red and 5 blue balls, two draws without putting back.
  • P(R then B) = 4/9 × 5/8 = 5/18. P(BB) = 5/9 × 4/8 = 5/18.
  • The probabilities on the second set of branches change because the basket has changed.
RefFormula sheetKey results

Quick reference

IdeaResult
Range0 ≤ P(E) ≤ 1
Impossible / certainP = 0 / P = 1
Experimentaltimes event occurred ÷ number of trials
Theoreticalfavourable ÷ possible (equally likely)
ComplementP(not E) = 1 − P(E)
Two dice36 outcomes
Three coins8 outcomes
Independent trialsmultiply along a tree path

Common slips

  • Listing HT and TH as the same outcome.
  • Forgetting that outcomes must be equally likely before using favourable ÷ possible.
  • Believing a run of results “makes up” for itself.
  • Treating drawing without replacement as if the basket stayed the same.