# Lecture 2: Story Proofs, Axioms of Probability | Statistics 110 | Summary and Q&A

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April 29, 2013
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Harvard University
Lecture 2: Story Proofs, Axioms of Probability | Statistics 110

## TL;DR

Probability can be defined by a sample space S and a probability function P that assigns a number between 0 and 1 to each event in S, satisfying two axioms: the probability of the empty set is 0 and the probability of the entire sample space is 1.

## Key Insights

• 👾 Probability can be defined using a sample space and probability function.
• 👾 The probability function assigns probabilities to events in the sample space.
• 😆 Two axioms, the probability of the empty set being 0 and the probability of the entire sample space being 1, must be satisfied.
• 🍹 The probability of a union of disjoint events is equal to the sum of their individual probabilities.

## Transcript

SPEAKER: OK. So, as far as clarifications, and hints and comments, and so on on the homework, these are kind of general comments. One is don't lose your common sense. That doesn't mean you can rely only on common sense, because we're going to see over and over again in this course a lot of counterintuitive results that may seem to defy common sense... Read More

### Q: What is a sample space?

A sample space is the set of all possible outcomes of an experiment.

### Q: What does the probability function do?

The probability function assigns a number between 0 and 1 to each event in the sample space, representing the likelihood of that event occurring.

### Q: What are the two axioms of probability?

The first axiom states that the probability of the empty set is 0. The second axiom states that the probability of the union of disjoint events is equal to the sum of the probabilities of each individual event.

### Q: How is probability defined for non-equally likely outcomes?

With the non-naive definition of probability, the probability function assigns probabilities to events based on their likelihood, without assuming that all outcomes are equally likely.

## Summary

In this video, the speaker provides clarifications and hints for the homework and comments on the importance of common sense and reasoning in probability. He also discusses the concept of labeling in counting problems and provides a story proof for several identities related to binomial coefficients. Lastly, he introduces the non-naive definition of probability, which involves a probability space consisting of a sample space and a probability function that follows two axioms.

### Q: What is the speaker's advice regarding calculators and common sense in probability problems?

The speaker advises using common sense when approaching probability problems and not to rely solely on calculators. While calculators can be used on homework, they are not allowed on exams. It is important to determine when using a calculator is necessary and to simplify calculations when possible.

### Q: What are the benefits of leaving large numbers like "52 choose 5" as self-annotating expressions?

Leaving expressions like "52 choose 5" can provide helpful context in understanding the problem and its meaning. While the actual number may be difficult to comprehend, the self-annotating expression provides information about choosing 5 items out of a total of 52. It is a convenient way to represent the problem and its solution.

### Q: How does the concept of labeling apply in counting problems involving people and objects?

In counting problems, labeling is a useful approach for distinguishing between individuals or objects. For example, in a group of people or animals, assuming each individual has a unique label or ID number can be helpful in solving counting problems. Similarly, labeling objects like balls in a jar can provide clarity and prevent confusion.

### Q: What is the significance of the statement "n choose k equals n choose n minus k"?

This statement implies that choosing k items out of n is the same as choosing n-k items out of n. The speaker provides a story proof to illustrate this concept. For example, if you choose 4 people out of a group of 10, it is the same as choosing the remaining 6 people out of the same group. This identity is a useful fact and can be easily understood through interpretation rather than manipulation of factorials.

### Q: How can Vandermonde's identity be proven using a story proof?

Vandermonde's identity, which states that (m + n choose k) is equal to the sum of [(m choose j) * (n choose k-j)], can be proven using a story proof. The speaker suggests imagining two groups of people, one with m members and one with n members. Selecting k people total involves choosing j people from the first group and (k-j) people from the second group. By considering all possible combinations of j and (k-j), the identity can be understood and proven through reasoning about selecting people from different groups.

### Q: What are the two axioms of probability in the non-naive definition of probability?

The two axioms of probability are: (1) the probability of the empty set is 0, and (2) the probability of a countable union of disjoint events is equal to the sum of their individual probabilities. The first axiom reflects the fact that impossible events have probability 0, while the second axiom allows for combining probabilities of non-overlapping events. These two axioms form the basis for all probability calculations and theorems.

## Takeaways

In probability, it is important to use common sense and reasoning to guide problem-solving. Leaving self-annotating expressions can provide helpful context and understanding. Labeling individuals and objects can assist in counting problems. Story proofs offer intuitive interpretations and explanations for concepts and identities. The non-naive definition of probability involves a probability space with a sample space and a probability function satisfying two axioms: probability 0 for the empty set and additivity for non-overlapping events. These simple axioms form the foundation for probability calculations.

## Summary & Key Takeaways

• Probability can be defined using a sample space S and a probability function P.

• The probability function assigns a number between 0 and 1 to each event in the sample space.

• Two axioms must be satisfied: the probability of the empty set must be 0 and the probability of the entire sample space must be 1.