1. Poisson Distribution (Not in Reif’s book)
Figure Intensity as a function of viewing angle θ (or position on the screen) for (a) two slits, (b) six slits. For a diffraction grating, the number of slits is very large (≈104) and the peaks are narrower still.

2. The Poisson Probability Distribution
"Researches on the probability of criminal civil verdicts" 1837 
Looked at the form of the binomial distribution
When the Number of
Trials is Large. 
He derived the cumulative Poisson distribution as the
Limiting case of the
Binomial when the
probability p of success
tends to zero.
Simeon Denis Poisson

3. The Poisson Probability Distribution
"Researches on the probability of criminal civil verdicts" 1837 
Looked at the form of the binomial distribution
When the Number of
Trials is Large. 
He derived the cumulative Poisson distribution as the
Limiting case of the
Binomial when the
probability p of success
tends to zero.
Simeon Denis Poisson
Simeon Denis
“Fish”!

4. The Poisson Distribution
Poisson Distribution: An approximation to the binomial distribution for the SPECIAL CASE when the average number (mean µ) of successes is very much smaller than the possible number n. i.e. µ << n because p << 1.
This distribution is important for the study of such phenomena as radioactive decay. This distribution is NOT necessarily symmetric! Data are usually bounded on one side & not the other.
An advantage of this distribution is that σ2 = μ
µ = 10.0
σ = 3.16
µ = 1.67
σ = 1.29

5. The Poisson Distribution Models Counts.
If events happen at a constant rate over time, the Poisson Distribution gives
The Probability of X Number of
Events Occurring in a time T.
This distribution tells us the
Probability of All Possible Numbers of
Counts, from 0 to Infinity.
If X= # of counts per second, then the Poisson probability that X = k (a particular count) is: l
λ ≡ the average number of counts per second.

6. Mean and Variance for the Poisson Distribution
It’s easy to show that for this distribution,
The Mean is:
Also, it’s easy to show that The Variance is: l L
 The Standard Deviation is:
 For a Poisson Distribution, the variance and mean are equal!

7. More on the Poisson Distribution
Terminology: A “Poisson Process”
The Poisson parameter  can be given as the mean number of events that occur in a defined time period OR, equivalently,  can be given as a rate, such as  = 2 events per month.  must often be multiplied by a time t in a physical process
(called a “Poisson Process” )
μ = t σ = t

8. Answer: If X = # calls in 1.5 hours, we want
Example
1. If calls to your cell phone are a Poisson process
with a constant rate  = 2 calls per hour, what
is the probability that, if you forget to turn your
phone off in a 1.5 hour class, your phone rings
during that time?
Answer: If X = # calls in 1.5 hours, we want
P(X ≥ 1) = 1 – P(X = 0) p
P(X ≥ 1) = 1 – .05 = 95% chance

9. The students & the instructor in the class will not be very
Example Continued
2. How many phone calls do you expect
to get during the class?
<X> = t = 2(1.5) = 3
Editorial comment:
The students & the instructor in the class will not be very
happy with you!!

10. Poisson Distribution to hold:
Conditions Required
for the
Poisson Distribution to hold: l
1. The rate is a constant, independent of time.
2. Two events never occur at exactly the same time.
3. Each event is independent. That is, the
occurrence of one event does not make the
next event more or less likely to happen. 10 10

11. λ = (5 defects/hour)*(0.25 hour) =
Example
A production line produces 600 parts per hour with an average of 5 defective parts an hour. If you test every part that comes off the line in 15 minutes,what is the probability of finding no defective parts (and incorrectly concluding that your process is perfect)?
λ = (5 defects/hour)*(0.25 hour) =
λ = 1.25
 p(x) = (xe-)/(x!)
x = given number of defects
P(x = 0) = (1.25)0e-1.25)/(0!)
= e-1.25 = 0.287
= 28.7%

12. Comparison of the Binomial & Poisson Distributions with Mean μ = 1
Clearly, there is not much difference between them!
For N Large & m Fixed:
Binomial  Poisson

13. Poisson Distribution: As λ (Average # Counts) gets large, this also approaches a Gaussian
λ = 15
λ = 5
λ = 35
λ = 25