 Probability is a branch of mathematics that deals with the study of random events. It plays an important role in various fields such as finance, sports, weather forecasting, and many more. The Law of Large Numbers is an important concept in probability theory that explains how, as the sample size increases, the average of a sample will be closer to the true population mean. In this article, we will take a closer look at the Law of Large Numbers, types of probability, using probability in real-life scenarios, limitations of the Law of Large Numbers, and alternative probability theories.

## What is the Law of Large Numbers?

The Law of Large Numbers is a concept in probability theory that states that as the sample size of a random variable increases, the average of the sample will tend to approach the true population mean. In simpler terms, it means that the larger the sample size, the more accurate the results will be.

• Statistics.
• Finance.
• Engineering.

### Explanation of how the Law of Large Numbers works:

The Law of Large Numbers works by taking the average of a large sample size to estimate the true population mean. This is based on the assumption that the random variable has a finite mean. As the sample size increases, the average tends to converge to the true population mean. The convergence can be proved mathematically using the Central Limit Theorem.

### Examples of the Law of Large Numbers in action:

#### Tossing a fair coin:

As the number of coin tosses increases, the theoretical probability of getting a 50:50 ratio of heads and tails becomes more precise.

#### Rolling a fair dice:

As the number of dice rolls increases, the theoretical probability of getting a number between 1 and 6 becomes more accurate.

## Types of Probability

#### Probability can be classified into three types:

• Classical.
• Empirical.
• and Subjective.

Each type of probability is used to predict the likelihood of an event occurring.

### Definition of each type of probability:

#### Classical Probability:

This type of probability is based on the assumption of equally likely outcomes. For instance, when flipping a coin, there are two equally likely outcomes–heads or tails.

#### Empirical Probability:

This type of probability is based on the results of an experiment. For instance, if you are tossing a coin and getting more heads than tails, your empirical probability for heads will be higher than tails.

#### Subjective Probability:

This type of probability is based on personal judgment or opinions. For instance, your subjective probability for a certain candidate winning an election might differ from that of another individual.

### Examples of each type of probability in various contexts:

#### Classical Probability:

The probability of pulling a card from a full deck of cards is 1/52.

#### Empirical Probability:

The probability of getting an A grade in a test is 20%, based on previous test results.

#### Subjective Probability:

The probability of rain tomorrow is 50%, based on personal judgment.

## Using Probability in Real-life Scenarios

Probability is used in various fields to make predictions about the likelihood of an event occurring. Here are some examples of using probability in real-life scenarios:

### Applications of probability in different fields:

#### Finance:

Probability is used in finance for risk assessment, portfolio management, and financial forecasting.

#### Sports:

Probability is used in sports for predicting game outcomes, player performance, and betting odds.

#### Weather Forecasting:

Probability is used in weather forecasting to predict the likelihood of severe weather conditions.

### Examples of probability in daily life:

• Predicting traffic times for commute to work.
• Predicting the probability of winning a lottery.
• Predicting the time taken to complete a task.

## Limitations of the Law of Large Numbers

Although the Law of Large Numbers is a useful concept for estimating the true population mean, it has its limitations. Here are some limitations of the Law of Large Numbers:

### Examining the scenarios where the Law of Large Numbers may not be applicable:

• When the random variable does not have a finite mean.
• When the sample is not random and unbiased.
• When the sample size is not large enough.

### Introduction to alternative probability theories such as the Central Limit Theorem:

The Central Limit Theorem is a probability theory that can be used when the sample size is small or when the random variable does not follow a normal distribution. It states that the sum of a large number of independent random variables will have an approximately normal distribution.

## Conclusion

Probability is an important concept that has numerous applications in various fields. The Law of Large Numbers explains how, as the sample size increases, the average of a sample will be closer to the true population mean. We also explored the different types of probability, using probability in real-life scenarios, and alternative probability theories such as the Central Limit Theorem. As students of mathematics, understanding probability is crucial for our success.

## FAQs

### Q.           What is probability and why is it important in mathematics?

Probability is a branch of mathematics that deals with the study of random events. It is important in mathematics because it helps us predict the likelihood of an event occurring.

### Q.          How is probability used in different fields?

Probability is used in various fields, including finance, sports, weather forecasting, and many more. It is used to make predictions about the likelihood of an event occurring.

### Q.         What is the difference between classical, empirical, and subjective probability?

Classical probability is based on the assumption of equally likely outcomes. Empirical probability is based on the results of an experiment. Subjective probability is based on personal judgment or opinions.

### Q.          What are the limitations of the Law of Large Numbers and what are some alternative probability theories I can use?

The Law of Large Numbers may not be applicable when the random variable does not have a finite mean, the sample is not random and unbiased, or the sample size is not large enough. The Central Limit Theorem is an alternative probability theory that can be used in such cases.