What is the Poisson distribution and when is it used in Probability and Statistics 2?

The Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space. It is used in Probability and Statistics 2, particularly in the Cambridge International A Levels curriculum, to model scenarios where events occur independently and at a constant average rate. Common applications include modeling the number of phone calls received by a call center in an hour or the number of decay events per unit time from a radioactive source.

How do you calculate the probability of an event using the Poisson distribution?

To calculate the probability of observing exactly k events in a Poisson distribution, you use the formula: P(X = k) = (λ^k * e^(-λ)) / k!, where λ is the average rate of occurrence, e is the base of the natural logarithm (approximately 2.71828), and k! is the factorial of k. This formula is essential for solving problems in the Probability and Statistics 2 syllabus of the Cambridge International A Levels.

What are the key properties of the Poisson distribution?

The key properties of the Poisson distribution include: 1) It is defined for non-negative integers (k = 0, 1, 2, ...). 2) The mean and variance of the distribution are both equal to λ. 3) It is suitable for modeling rare events. 4) Events are independent, and the probability of more than one event occurring in an infinitesimally small interval is negligible. These properties are crucial for understanding its applications in Probability and Statistics 2.

How does the Poisson distribution relate to the exponential distribution?

The Poisson distribution is related to the exponential distribution in that the time between events in a Poisson process follows an exponential distribution. If events occur according to a Poisson distribution with rate λ, then the time between consecutive events is exponentially distributed with parameter λ. This relationship is often explored in the Probability and Statistics 2 course of the Cambridge International A Levels to understand the connection between different types of probability distributions.

What are some common misconceptions about the Poisson distribution?

A common misconception about the Poisson distribution is that it can be used for any type of event occurrence. However, it is only appropriate when events occur independently, at a constant average rate, and the probability of more than one event occurring in a very short interval is negligible. Another misconception is that it can be used for large values of λ, but in such cases, the normal distribution is often a better approximation. Understanding these nuances is important for students studying Probability and Statistics 2 in the Cambridge International A Levels curriculum.

Why is "Forgetting to scale $\lambda$ when interval changes" a common mistake in The Poisson distribution ?

Why it happens: Quoted rate may be per hour but question asks per minute. How to avoid it: Scale linearly. Rate 12/hour → 0.2/minute → 0.2 for Po. Source: 9709 Examiner Reports 2022-2024

Why is "Using Poisson approximation when $p$ not small" a common mistake in The Poisson distribution ?

Why it happens: Habit. How to avoid it: Conditions: n ≥ 50 AND p ≤ 0.1. Otherwise, use normal approximation. Source: 9709 Examiner Reports 2022-2024

Cambridge International A Levels Mathematics (9709)

The Poisson distribution

0% Complete

3 tasks remaining

Short Notes - The Poisson distribution

Detailed Study Notes

Detailed notes on Probability and Statistics 2 - Paper 6 for Cambridge International A Levels Mathematics, covering key concepts, explanations, examples, and exam-focused revision points.

Poisson Distribution Study Notes — Cambridge International A Level Mathematics 9709 S2 (2024-2026 syllabus)

Modelling rare events. PMF, mean=variance= $\lambda$ . Approximation to binomial. Sum of independent Poissons.

At a glance

PMF: $P(X = r) = \frac{e^{-\lambda} \lambda^r}{r!}$ .
Mean = variance = $\lambda$ (DISTINCTIVE).
Sum of Poissons is Poisson (sum of $\lambda$ s).
Approximation to $B(n, p)$ when $n$ large, $p$ small.
Scale $\lambda$ linearly with interval.

What you’ll learn

Mapped to the Cambridge International A Level 9709 syllabus (2024-2026).

S2.1.1 — Use Poisson PMF.
S2.1.2 — Use Poisson approximation to binomial.
S2.1.3 — Sum independent Poissons.

Poisson PMF

Rare events in fixed interval.

Model. Events occurring randomly in time or space at constant average rate $\lambda$ . $X$ = number in fixed interval.

Notation. $X \sim \text{Po}(\lambda)$ .

PMF. $P(X = r) = \frac{e^{-\lambda} \lambda^r}{r!}$ for $r = 0, 1, 2, \ldots$

Mean and variance. $E(X) = \text{Var}(X) = \lambda$ . DISTINCTIVE — both equal $\lambda$ .

Conditions.

Events independent.
Rate constant.
Events occur singly (not in clusters).

Scaling. If rate is $\lambda$ per minute, then in $t$ minutes use $\lambda t$ .

Example. $X \sim \text{Po}(3.5)$ . $P(X = 2) = \frac{e^{-3.5} (3.5)^2}{2!} \approx 0.185$ .

Cambridge tip. State $X \sim \text{Po}(\lambda)$ at start.

Poisson counts events in a fixed interval when they occur independently at a constant average rate. Its signature is that the mean and the variance are the same number, λ.

λ is the only parameter, so it fixes both the centre and the spread. That is why a data set with mean far from its variance is not Poisson.

$X \sim \text{Po}(\lambda)$ .
Mean = variance = $\lambda$ .
Scale linearly with interval.

See the full worked example for the poisson distribution →

Sum of independent Poissons

Sum is Poisson.

Result. $X \sim \text{Po}(\lambda_1)$ , $Y \sim \text{Po}(\lambda_2)$ , independent. $X + Y \sim \text{Po}(\lambda_1 + \lambda_2)$ .

Use. Combining multiple Poisson sources or extending intervals.

Example. Calls at switchboard A: $\text{Po}(2)$ /min. At B: $\text{Po}(3)$ /min. Combined: $\text{Po}(5)$ /min.

Multiple intervals. Rate $\lambda$ /min × 5 mins = $5\lambda$ .

Cambridge tip. Explicit independence + Poisson distribution required.

Adding independent Poisson counts gives another Poisson whose parameter is the sum. Both the mean and the variance add, which is consistent because for a Poisson they are the same number.

Sum of independent Poissons.
Add rate parameters.
Combines multiple sources or intervals.

See the full worked example for the poisson distribution →

Poisson approximation to binomial

Rare events in many trials.

When. $X \sim B(n, p)$ with $n$ LARGE ( $\geq 50$ ) and $p$ SMALL ( $\leq 0.1$ ).

Approximation. $X \approx \text{Po}(np)$ .

Why it works. As $n \to \infty$ and $p \to 0$ with $np$ fixed, binomial → Poisson. Sufficient: $n \geq 50$ , $p \leq 0.1$ .

No continuity correction. Both binomial and Poisson are discrete.

Example. $X \sim B(200, 0.02)$ . $np = 4$ . Use $X \approx \text{Po}(4)$ . $P(X \leq 3) = P(0) + P(1) + P(2) + P(3) = e^{-4}(1 + 4 + 8 + \frac{32}{3}) \approx 0.434$ .

Cambridge tip. Always state and verify conditions.

When n is large and p is small the binomial is awkward to compute but Po(np) is easy, and the two are almost indistinguishable. Quote the conditions — the mark scheme wants them.

The two approximations answer different situations: Poisson for rare events in many trials, normal for a bulky binomial with both np and n(1 − p) comfortably above 5.

Conditions $n \geq 50$ , $p \leq 0.1$ .
$X \approx \text{Po}(np)$ .
No continuity correction.

See the full worked example for the poisson distribution →

Quick recap

$X \sim \text{Po}(\lambda)$ with $E = \text{Var} = \lambda$ .
Sum of independent Poissons is Poisson.
Binomial ≈ Poisson for large $n$ , small $p$ .
Scale $\lambda$ with interval length.

Memorise this

Verbatim phrases and definitions Cambridge mark schemes credit.

Poisson PMF.
Mean = variance = $\lambda$ .
Sum of Poissons is Poisson.
Approximation conditions ( $n \geq 50$ , $p \leq 0.1$ ).

How it’s examined

Poisson is heavily tested on S2 — typically 12-15 marks. Most-tested: PMF (6-7 marks), approximation to binomial (7 marks), sum of independent Poissons (5 marks).

Sources: Cambridge International A Level Mathematics 9709 syllabus (2024-2026); 9709 Examiner Reports 2022-2024; 9709/62 May/Jun 2024 question paper and mark scheme. Last reviewed 2026-05-11.

Take this whole topic with you

Step-by-step worked examples — The Poisson distribution

Step-by-step solutions to past-paper-style questions on the poisson distribution , written exactly the way a tutor would explain them at the board.

1Poisson probability (6 marks)
Extended• Adapted from 9709/62 May/Jun 2024• Poisson
Question
$X \sim \text{Po}(3.5)$ . Find $P(X = 2)$ . (6 marks)
Step-by-step solution
1. Step 1
  Poisson PMF. $P(X = r) = \frac{e^{-\lambda} \lambda^r}{r!}$ .
2. Step 2
  Substitute. $\lambda = 3.5$ , $r = 2$ .
  $P(X = 2) = \dfrac{e^{-3.5} (3.5)^2}{2!} = \dfrac{e^{-3.5} \times 12.25}{2}$
3. Step 3
  Evaluate.
  $\approx 0.0302 \times 12.25 / 2 \approx 0.185$
Answer
$P(X = 2) \approx 0.185$ .
2Poisson approximation to binomial (7 marks)
Extended• approximation
Question
$X \sim B(200, 0.02)$ . Use Poisson approximation to find $P(X \leq 3)$ . (7 marks)
Step-by-step solution
1. Step 1
  Check conditions. $n$ large ( $\geq 50$ ), $p$ small ( $\leq 0.1$ ), $np = 4$ moderate. ✓
2. Step 2
  Approximate. $X \approx \text{Po}(\lambda)$ with $\lambda = np = 4$ .
3. Step 3
  Compute. $P(X \leq 3) = P(0) + P(1) + P(2) + P(3) = e^{-4}(1 + 4 + 8 + 32/6) \approx 0.0183 \times 17.33 \approx 0.434$ .
Answer
$P(X \leq 3) \approx 0.434$ .
3Sum of independent Poissons (5 marks)
Extended• sum
Question
Phone calls at switchboard A follow $\text{Po}(2)$ per minute; at B, $\text{Po}(3)$ per minute. Find probability of exactly 4 calls combined in one minute. (5 marks)
Step-by-step solution
1. Step 1
  Sum of independent Poissons is Poisson with mean = sum of means.
  $X_A + X_B \sim \text{Po}(5)$
2. Step 2
  Apply PMF.
  $P(X = 4) = \dfrac{e^{-5} \cdot 5^4}{4!} = \dfrac{e^{-5} \cdot 625}{24} \approx 0.175$
Answer
$\approx 0.175$ .

Key Formulae — The Poisson distribution

The formulae you need to memorise for the poisson distribution on the Cambridge International A Level 9709 paper, with every variable defined in plain English and a note on when to use it.

Poisson PMF
$P(X = r) = \dfrac{e^{-\lambda} \lambda^r}{r!}, \quad r = 0, 1, 2, \ldots$
$\lambda$
mean (also variance)
When to use
Modelling number of rare events in fixed interval (time/space).
Poisson mean and variance
$E(X) = \text{Var}(X) = \lambda$
When to use
Distinctive: mean equals variance.
Sum of independent Poissons
$X + Y \sim \text{Po}(\lambda_1 + \lambda_2) \quad (\text{when } X \sim \text{Po}(\lambda_1),\, Y \sim \text{Po}(\lambda_2) \text{ independent})$
When to use
Combining multiple Poisson sources.
Poisson approximation to binomial
$B(n, p) \approx \text{Po}(np) \quad (n \geq 50,\; p \leq 0.1)$
When to use
Rare events in many trials.

Key Definitions and Keywords — The Poisson distribution

Definitions to memorise and the exact keywords mark schemes credit for the poisson distribution answers — sharpened from recent examiner reports for the 2026 Cambridge International A Level 9709 sitting.

Poisson distribution
Examiner keyword
Number of rare events in fixed interval. $X \sim \text{Po}(\lambda)$ . Mean = variance = $\lambda$ .
Rate parameter $\lambda$
Examiner keyword
Expected number of events per unit interval.

Common Mistakes and Misconceptions — The Poisson distribution

The traps other students keep falling into on the poisson distribution questions — taken from recent Cambridge International A Level 9709 examiner reports and mark schemes — and how to avoid them.

✕Forgetting to scale $\lambda$ when interval changes
9709 Examiner Reports 2022-2024
Why it happens
Quoted rate may be per hour but question asks per minute.
How to avoid it
Scale $\lambda$ linearly. Rate 12/hour → 0.2/minute → 0.2 for $\text{Po}$ .
✕Using Poisson approximation when $p$ not small
9709 Examiner Reports 2022-2024
Why it happens
Habit.
How to avoid it
Conditions: $n \geq 50$ AND $p \leq 0.1$ . Otherwise, use normal approximation.