# Lecture 11: The Poisson distribution | Statistics 110 | Summary and Q&A

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April 29, 2013
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Harvard University
Lecture 11: The Poisson distribution | Statistics 110

## TL;DR

The Poisson distribution is a widely used distribution in statistics for counting events that occur at a low rate but in a large number of trials. One common mistake in probability is confusing a random variable with its distribution.

## Key Insights

• ❓ Understanding the difference between a distribution and a random variable is crucial in probability theory.
• 😘 The Poisson distribution is a widely used model for counting events that occur at a low rate but in a large number of trials.
• 🌍 The Poisson distribution is an approximation for real-world scenarios where each event is unlikely but there are a large number of possibilities.
• 😕 Confusing a random variable with its distribution can lead to common probability mistakes.
• 🥳 The Poisson distribution can be used to approximate the number of triple birthday matches in a group of people.

## Transcript

So coming back into what we were doing with discrete distributions. And I wanted to mention one common. Kind of the most common or fundamental common mistake in probability. Because a couple of the TFs mentioned that this is coming up on the homework, which I'm not surprised about because this is very fundamental. It's trying to understand the diff... Read More

### Q: What is the difference between a distribution and a random variable?

A random variable is a variable that can take on different values based on the outcome of a random event, while a distribution describes the probabilities associated with each value that the random variable can take.

### Q: Why is it important to understand the difference between a distribution and a random variable?

Understanding the difference between a distribution and a random variable is crucial for correctly calculating probabilities and making accurate statistical inferences. Confusing the two can lead to incorrect calculations and misinterpretation of data.

### Q: Can the sum of two random variables be calculated by adding their probability mass functions (PMFs)?

No, adding the probability mass functions of two random variables does not give the correct result for their sum. The sum of random variables is a separate random variable that needs to be calculated using different methods, such as conditioning or convolution.

### Q: How does the Poisson distribution relate to counting events in real-world scenarios?

The Poisson distribution is often used as an approximation for counting events that occur at a low rate but in a large number of trials. It can be applied to various scenarios, such as the number of emails received per hour or the number of earthquakes in a year.

## Summary

In this video, the lecturer discusses the common mistake of confusing a random variable with its distribution, which he terms "sympathetic magic." He explains that adding random variables is not the same as adding probability mass functions (PMFs) and that performing operations on the PMFs does not make sense in relation to the random variables. He also introduces the Poisson distribution, which is the most important discrete distribution in statistics. The Poisson distribution is named after the French mathematician Poisson and has a probability mass function of e^-lambda * lambda^k / k!, where lambda is a positive constant. The lecturer explains that the Poisson distribution is commonly used to model situations where events are being counted, such as the number of emails received in an hour or the number of earthquakes in a year. He also discusses the Poisson approximation, which states that the binomial distribution converges to the Poisson distribution when the number of trials is large and the probability of success is small.

### Q: What is the difference between a random variable and its distribution?

The mistake of confusing a random variable with its distribution is referred to as "sympathetic magic." The random variable refers to the actual values that can be observed or measured, while the distribution describes the probabilities associated with those values. Confusing the two can lead to misconceptions about adding random variables and performing operations on probability mass functions (PMFs), which do not align with the properties of random variables.

### Q: Why is it incorrect to add PMFs when summing random variables?

When summing random variables, one must be cautious not to confuse the sum of the variables with the sum of their PMFs. Adding random variables implies combining the individual values of the variables, whereas adding PMFs refers to adding the probabilities associated with each value. These two operations are fundamentally different and do not produce the same results. Additionally, adding probabilities can lead to values exceeding 1 or being less than 0, which is invalid.

### Q: How does the Poisson distribution differ from other discrete distributions?

The Poisson distribution is unique because it has an unbounded range, meaning it can take any non-negative integer value. This is in contrast to other discrete distributions, such as the binomial distribution, which is bounded by the number of trials. The Poisson distribution is also a one-parameter distribution, with lambda being the most common parameter. Lambda represents the rate parameter and can be any positive real number.

### Q: What is the expected value of a Poisson distribution?

The expected value of a Poisson distribution is equal to the parameter lambda. This means that the mean or average value of a Poisson distribution is lambda. The expected value is calculated as the sum of all the possible values (k) multiplied by their respective probabilities. In the case of the Poisson distribution, the formula simplifies to lambda, which is a useful and easy-to-remember result.

### Q: How is the Poisson distribution used in practice?

The Poisson distribution is the most widely used discrete distribution for modeling real-world data involving counting events. It is commonly applied in situations where there is a large number of trials, each with a small probability of success. Some examples include counting the number of emails received in an hour, the number of chocolate chips in a cookie, or the number of earthquakes in a year. The Poisson distribution provides a reasonable first approximation for these scenarios, although it may not be an exact fit in every case.

### Q: How does the Poisson approximation work in the context of the binomial distribution?

The Poisson approximation is used to approximate the binomial distribution when the number of trials is large and the probability of success is small. In this case, the product of the number of trials and the probability of success, denoted as lambda (= np), is held constant. The Poisson distribution provides a simpler and more manageable approximation compared to the binomial distribution. The approximation becomes even more accurate when the probability of success is very small.

### Q: In the Poisson approximation, why is it reasonable to assume weak dependence among the events?

While independence is ideal, the Poisson approximation is still applicable even with weakly dependent events. Weak dependence means that there is some subtle relationship or influence between the events, but it does not significantly impact the overall probabilistic outcomes. For example, if we have three people with the same birthday, knowing that the first three have the same birthday gives a slight advantage or head start to the fourth person, but it does not guarantee their birthday will match. The key point is that the probabilities are small, and the overall approximation still holds reasonably well.

### Q: How can the Poisson approximation be used to approximate the probability of triple birthday matches?

To approximate the probability of triple birthday matches, we can use the Poisson distribution. First, we calculate the expected value of the number of triple matches using indicator random variables and linearity. Then, by applying the Poisson approximation, we can find the probability of at least one triple match by subtracting the probability of zero matches from one. The resulting expression can be easily evaluated using a calculator or a computer, providing a simple and quick approximation for the desired probability.

## Takeaways

The most common mistake in probability is confusing a random variable with its distribution, known as "sympathetic magic." Understanding the difference between the two is crucial when working with probabilities and distributions. The Poisson distribution is the most important discrete distribution in statistics, commonly used to model situations involving counting events. The distribution is characterized by a single parameter, lambda, which represents the rate of occurrence. The Poisson distribution plays a significant role in the Poisson approximation, where the binomial distribution converges to the Poisson distribution under certain conditions. The Poisson approximation provides a simpler approach to complex problems and allows for quick and practical approximations of probabilities.

## Summary & Key Takeaways

• The Poisson distribution is a discrete distribution used to model events that occur at a low rate but in a large number of trials.

• One common mistake in probability is confusing a random variable with its distribution, which can lead to incorrect calculations.

• The Poisson distribution is a useful approximation for counting events, such as the number of emails received or the number of raindrops that fall, where each individual event is unlikely but there are a large number of possibilities.