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ICSE • Class 8 • Mathematics • Ch 25
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Probability

In ICSE Class 8 Mathematics, "Probability" provides an authoritative, mathematically rigorous master study guide investigating chance, uncertainty, random experiments, sample spaces, and classical theoretical probability. This comprehensive chapter explores Basic Concepts of Probability (Measure of likelihood or uncertainty of occurrence of an event; Deterministic experiments vs Random experiments: unpredictable individual outcomes under identical conditions), Sample Space ($S$: the set of all possible distinct outcomes of a random experiment; Sample points $n(S)$), Events ($E$: any subset of the sample space $S$; Elementary / simple event vs compound event), Classical / Theoretical Probability Axiom: $\mathbf{P(E) = \frac{n(E)}{n(S)} = \frac{\text{Number of Favorable Outcomes}}{\text{Total Number of Possible Outcomes}}}$, Fundamental Invariant Bounds of Probability ($0 \le P(E) \le 1$; Impossible Event: $P(\emptyset) = 0$ [e.g., rolling a 7 on a standard 6-sided die]; Sure / Certain Event: $P(S) = 1$ [e.g., getting a number $< 7$]), Complementary Events ($E$ and $\bar{E}$ / "not $E$": $P(E) + P(\bar{E}) = 1 \implies P(\bar{E}) = 1 - P(E)$), and Standard Classical Random Experiment Paradigms: 1. Coin Tossing (Single coin: $\{H, T\}, n=2$; Two coins tossed simultaneously: $\{HH, HT, TH, TT\}, n=4$; Three coins: $n=8$), 2. Rolling Standard Six-Sided Dice (Single die: $\{1, 2, 3, 4, 5, 6\}, n=6$; Prime numbers, even numbers, multiples; Pair of dice: $n=36$), 3. Standard Well-Shuffled Deck of 52 Playing Cards (4 suits of 13 cards: 26 Red [Hearts $\heartsuit$, Diamonds $\diamondsuit$], 26 Black [Spades $\spadesuit$, Clubs $\clubsuit$]; 12 Face / Court cards [4 Kings, 4 Queens, 4 Jacks]; 4 Aces), and 4. Colored Marbles, Slips, and Urn Problems aligned with the 2026–27 CISCE ICSE curriculum.

How Did a French Gambler's Unfinished Dice Game in 1654 Create the Mathematical Foundation of Modern Quantum Physics and Insurance?

In the summer of 1654, a notorious French nobleman and professional gambler named the Chevalier de Méré faced a crisis. He was playing a high-stakes dice game, but the game was suddenly interrupted and abandoned midway. How should the jackpot prize money be divided fairly based on each player's chance of winning? Confused, De Méré wrote a letter to the mathematical genius Blaise Pascal, who in turn wrote to Pierre de Fermat. Through their feverish letters, Pascal and Fermat invented the MATHEMATICAL THEORY OF PROBABILITY! What started as a dispute over dice became the mathematical language governing quantum mechanics (electron wave probability clouds), modern insurance life tables, weather forecasting algorithms, and stock market Wall Street risk models! Why can a probability NEVER be negative and NEVER be greater than 1? What is the probability of drawing a black face card from a standard deck of 52 cards? Let's master probability.

Why This Chapter Matters

Probability is the mathematical foundation of all data science, artificial intelligence machine learning decision trees, genetic inheritance Punnett squares, actuarial insurance pricing, and clinical pharmaceutical trial drug evaluations. Mastering classical probability is a core requirement of ICSE secondary mathematics.

Before You Begin (Prerequisites)

  • Fractions and decimals simplification.
  • Sets notation (sample space as a set).
  • Basic arithmetic counting.

What You Will Learn (Core Objectives)

  • Distinguish between deterministic and random experiments.
  • List complete sample spaces ($S$) for coin tosses, dice rolls, and card draws.
  • Apply the classical probability formula: $P(E) = \frac{n(E)}{n(S)}$.
  • Verify that $0 \le P(E) \le 1$, with $P=0$ for impossible and $P=1$ for certain events.
  • Utilize complementary probability: $P(\text{not } E) = 1 - P(E)$.
  • Calculate probabilities of face cards, honor cards, and specific suits in a 52-card deck.

Chapter Roadmap & Progression

1 1. Random Experiments, Sample Space...
2 2. Classical Probability Axiom & Bo...
3 3. The Anatomy of a 52-Card Deck
4 4. Compound Coin & Dice Probability

Complete Concept Guide (100% Curriculum Coverage)

1. Random Experiments, Sample Space & Events

Understand
A. Core Definitions:
  • Random Experiment: An experiment where all possible outcomes are known in advance, but the exact outcome of any specific trial cannot be predicted with certainty.
  • Sample Space ($S$): The set of ALL possible outcomes of a random experiment.
    • Tossing 1 coin: $S = \{H, T\} \implies n(S) = 2$.
    • Tossing 2 coins: $S = \{HH, HT, TH, TT\} \implies n(S) = 4$.
    • Rolling 1 die: $S = \{1, 2, 3, 4, 5, 6\} \implies n(S) = 6$.
  • Event ($E$): Any subset of the sample space ($E \subseteq S$).

2. Classical Probability Axiom & Bounds

The Probability Axiom
A. The Formula:
$$\mathbf{P(E) = \frac{n(E)}{n(S)} = \frac{\text{Number of outcomes favorable to } E}{\text{Total number of possible outcomes in } S}}$$
B. The Universal Invariant Bounds:
$$\mathbf{0 \le P(E) \le 1}$$
  • Impossible Event: An event that can never happen: $\mathbf{P(\emptyset) = 0}$ (e.g., rolling an 8 on a standard die).
  • Certain / Sure Event: An event that is guaranteed to happen: $\mathbf{P(S) = 1}$ (e.g., rolling a number $< 7$ on a standard die).
  • Complementary Event ($\\bar{E}$ or "not $E$"): $$\mathbf{P(E) + P(\bar{E}) = 1 \implies P(\bar{E}) = 1 - P(E)}$$

3. The Anatomy of a 52-Card Deck

52 Cards

Total cards in a standard deck: $n(S) = 52$.

  • $2$ Colors ($26$ each):
    • $26$ Red Cards: Hearts ($\heartsuit$, 13) and Diamonds ($\diamondsuit$, 13).
    • $26$ Black Cards: Spades ($\spadesuit$, 13) and Clubs ($\clubsuit$, 13).
  • $4$ Suits ($13$ cards each): Each suit consists of: $$\text{Ace, 2, 3, 4, 5, 6, 7, 8, 9, 10, Jack, Queen, King}$$
  • Face Cards (Court Cards): Kings, Queens, and Jacks. $$\mathbf{\text{Total Face Cards} = 3 \times 4 = 12} \quad (6\text{ Red} + 6\text{ Black})$$
  • Aces: $4$ Aces (Aces are NOT face cards!).

4. Compound Coin & Dice Probability

Coins & Dice
A. Two Coins Tossed Simultaneously:

Sample space: $S = \{HH, HT, TH, TT\} \implies n(S) = 4$.

  • $P(\text{Exactly 1 Head}) = \frac{\{HT, TH\}}{4} = \frac{2}{4} = \mathbf{\frac{1}{2}}$.
  • $P(\text{At least 1 Head}) = \frac{\{HH, HT, TH\}}{4} = \mathbf{\frac{3}{4}}$.
  • $P(\text{At most 1 Head}) = \frac{\{TT, HT, TH\}}{4} = \mathbf{\frac{3}{4}}$.
B. Single Die:

$S = \{1, 2, 3, 4, 5, 6\}$. Prime numbers: $\{2, 3, 5\} \implies P(\text{Prime}) = \frac{3}{6} = \mathbf{\frac{1}{2}}$. *(Note: $1$ is NOT prime!)*.

Key Formulas, Identities & Theorems

Classical Theoretical Probability
$$P(E) = \frac{n(E)}{n(S)}$$
Number of favorable outcomes divided by total sample space outcomes.
Complementary Event Law
$$P(E) + P(\bar{E}) = 1 \iff P(\bar{E}) = 1 - P(E)$$
Probability of event occurring plus probability of it not occurring is 1.

Probability: The 0-to-1 Scale & 52-Card Deck Breakdown

Probability: The 0-to-1 Continuum & 52-Card Hierarchy THE PROBABILITY CONTINUUM [0, 1] P = 0 Impossible P = 0.5 (1/2) Even Chance P = 1 Certain P(E) = n(E) / n(S) P(E) + P(not E) = 1 ⇒ P(not E) = 1 - P(E) • Probability is NEVER negative and NEVER > 1 • Tossing 2 coins: {HH, HT, TH, TT} ⇒ n(S) = 4 52-CARD DECK HIERARCHY 26 RED CARDS Hearts (13) • Diamonds (13) 26 BLACK CARDS Spades (13) • Clubs (13) • 12 Face Cards (Court Cards): 4 Kings • 4 Queens • 4 Jacks (6 Red + 6 Black) • 4 Aces (1 per suit • Aces are NOT face cards!) P(Red Face Card) = 6 / 52 = 3 / 26 P(Ace) = 4 / 52 = 1 / 13 • P(Spade) = 13 / 52 = 1 / 4 0 ≤ P(E) ≤ 1 • P(IMPOSSIBLE)=0 • P(CERTAIN)=1 • 12 FACE CARDS IN DECK • P(not E)=1-P(E)

Chapter Summary & 10 Key Takeaways

Takeaway 1
A random experiment has known outcomes whose specific result cannot be predicted in advance.
Takeaway 2
Sample space S is the set of all possible outcomes of an experiment.
Takeaway 3
Probability formula: P(E) = n(E) / n(S) = (Favorable Outcomes) / (Total Outcomes).
Takeaway 4
Probability is strictly bounded: 0 <= P(E) <= 1.
Takeaway 5
An impossible event has P = 0; a certain/sure event has P = 1.
Takeaway 6
Complementary probability: P(not E) = 1 - P(E).
Takeaway 7
Tossing 2 coins produces 4 outcomes: {HH, HT, TH, TT}.
Takeaway 8
A single 6-sided die has sample space {1, 2, 3, 4, 5, 6}; prime outcomes are {2, 3, 5}.
Takeaway 9
A 52-card deck has 26 red, 26 black, 4 suits of 13 cards, 12 face cards, and 4 aces.
Takeaway 10
Aces are honor cards, not face cards.

Check Your Understanding (Diagnostic Practice Questions)

Diagnostic questions testing core conceptual clarity. Answers are hidden initially — solve each problem first, then click to reveal the step-by-step verified solution.

1
A standard die is thrown once. Find the probability of getting:
(a) a prime number, (b) a number greater than $4$, (c) a number divisible by $3$, (d) a number greater than $6$.
Reveal Answer & Explanation
Answer:

The sample space is $S = \{1, 2, 3, 4, 5, 6\} \implies n(S) = 6$.

• (a) Prime number: Prime outcomes are $\{2, 3, 5\}$ ($1$ is neither prime nor composite):

$$n(E) = 3 \implies P(\text{Prime}) = \frac{3}{6} = \mathbf{\frac{1}{2}}$$



• (b) Number greater than $4$: Favorable outcomes are $\{5, 6\}$:

$$n(E) = 2 \implies P(> 4) = \frac{2}{6} = \mathbf{\frac{1}{3}}$$



• (c) Divisible by $3$: Favorable outcomes are $\{3, 6\}$:

$$n(E) = 2 \implies P(\text{Div by 3}) = \frac{2}{6} = \mathbf{\frac{1}{3}}$$



• (d) Number greater than $6$: No face has a number $> 6$ (Impossible event):

$$n(E) = 0 \implies P(> 6) = \frac{0}{6} = \mathbf{0}$$

.


(a) Primes: $\{2, 3, 5\} \implies 3/6 = 1/2$. (b) $> 4: \{5, 6\} \implies 2/6 = 1/3$. (c) Div by 3: $\{3, 6\} \implies 2/6 = 1/3$. (d) Impossible: $0$.
2
Two coins are tossed simultaneously. Find the probability of getting:
(a) exactly one head, (b) at least one head, (c) at most one head.
Reveal Answer & Explanation
Answer:

The sample space is $S = \{HH, HT, TH, TT\} \implies n(S) = 4$.

• (a) Exactly one head: Outcomes are $\{HT, TH\}$:

$$n(E) = 2 \implies P(\text{Exactly 1 Head}) = \frac{2}{4} = \mathbf{\frac{1}{2}}$$



• (b) At least one head (1 or 2 heads): Outcomes are $\{HT, TH, HH\}$:

$$n(E) = 3 \implies P(\text{At least 1 Head}) = \mathbf{\frac{3}{4}}$$



• (c) At most one head (0 or 1 head): Outcomes are $\{TT, HT, TH\}$:

$$n(E) = 3 \implies P(\text{At most 1 Head}) = \mathbf{\frac{3}{4}}$$

.


Sample space has 4 outcomes. (a) 2/4 = 1/2. (b) 3/4. (c) 3/4.
3
A card is drawn at random from a well-shuffled pack of 52 playing cards. Find the probability of getting:
(a) a red face card, (b) an ace, (c) a spade, (d) a black queen.
Reveal Answer & Explanation
Answer:

Total outcomes $n(S) = 52$.

• (a) Red face card: There are 6 red face cards (King, Queen, Jack of Hearts and Diamonds):

$$P(\text{Red Face Card}) = \frac{6}{52} = \mathbf{\frac{3}{26}}$$



• (b) An ace: There are 4 aces in the deck:

$$P(\text{Ace}) = \frac{4}{52} = \mathbf{\frac{1}{13}}$$



• (c) A spade: There are 13 spade cards:

$$P(\text{Spade}) = \frac{13}{52} = \mathbf{\frac{1}{4}}$$



• (d) A black queen: There are 2 black queens (Queen of Spades and Queen of Clubs):

$$P(\text{Black Queen}) = \frac{2}{52} = \mathbf{\frac{1}{26}}$$

.


(a) $6/52 = 3/26$. (b) $4/52 = 1/13$. (c) $13/52 = 1/4$. (d) $2/52 = 1/26$.
4
A bag contains $5$ red balls, $8$ white balls, $4$ green balls, and $7$ black balls. If one ball is drawn at random, find the probability that it is: (a) black, (b) not green.
Reveal Answer & Explanation
Answer:

Step 1: Calculate total balls in bag:

$$n(S) = 5 + 8 + 4 + 7 = \mathbf{24\text{ balls}}$$



• (a) Black ball: $n(\text{Black}) = 7$:

$$P(\text{Black}) = \mathbf{\frac{7}{24}}$$



• (b) Not green ball:
Method 1: Balls that are not green $= 5 + 8 + 7 = 20$.

$$P(\text{Not Green}) = \frac{20}{24} = \mathbf{\frac{5}{6}}$$


Method 2: Using complementary probability:

$$P(\text{Green}) = \frac{4}{24} = \frac{1}{6} \implies P(\text{Not Green}) = 1 - \frac{1}{6} = \mathbf{\frac{5}{6}}$$

.


Total = 24. (a) $P(\text{Black}) = 7/24$. (b) $P(\text{Not Green}) = 1 - 4/24 = 20/24 = 5/6$.
5
If the probability of winning a badminton game is $0.78$, what is the probability of losing the game?
Reveal Answer & Explanation
Answer: Winning and losing are complementary events:
$$P(\text{Winning}) + P(\text{Losing}) = 1$$
$$P(\text{Losing}) = 1 - P(\text{Winning}) = 1 - 0.78 = \mathbf{0.22}$$.
$P(\text{Losing}) = 1 - 0.78 = 0.22$.
6
Can the probability of an event be $-0.5$ or $1.4$? Explain why or why not.
Reveal Answer & Explanation
Answer:

• No, neither value is possible.
• By the fundamental axioms of probability, the probability of any event $E$ must satisfy the invariant inequality:

$$\mathbf{0 \le P(E) \le 1}$$


• A probability can never be negative because the count of favorable outcomes $n(E) \ge 0$.
• A probability can never exceed $1$ because the number of favorable outcomes cannot exceed the total number of possible outcomes ($n(E) \le n(S)$).


Probability is strictly bounded between 0 and 1 inclusive ($0 \le P(E) \le 1$).
7
A box contains slips numbered from $1$ to $25$. One slip is drawn at random. Find the probability that the number on the slip is a multiple of $5$.
Reveal Answer & Explanation
Answer: Sample space $S = \{1, 2, 3, \dots, 25\} \implies n(S) = 25$.
Multiples of $5$ between $1$ and $25$ are: $\{5, 10, 15, 20, 25\}$.
$$n(E) = 5$$
$$P(\text{Multiple of 5}) = \frac{5}{25} = \mathbf{\frac{1}{5}}$$.
Multiples of 5: $\{5, 10, 15, 20, 25\}$. Probability is $5/25 = 1/5$.
8
Explain the difference between a Simple (Elementary) Event and a Compound Event with examples.
Reveal Answer & Explanation
Answer:

• Simple (Elementary) Event: An event having exactly one single sample point (outcome) from the sample space.
Example: In rolling a die ($S = \{1, 2, 3, 4, 5, 6\}$), getting the number $5$ (Event $E = \{5\}$) is an elementary event.
• Compound Event: An event having two or more sample points.
Example: Getting an even number on a die (Event $E = \{2, 4, 6\}$) is a compound event because it is formed by combining three elementary outcomes.


An elementary event contains exactly 1 outcome (e.g., $\{5\}$); a compound event contains 2 or more outcomes (e.g., $\{2, 4, 6\}$).
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