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How to Solve Probability Problems Using Expected Value

Probability problems are complex and often daunting for many people. However, understanding the concept of expected value can make solving probability problems much easier. In this article, we will explore the concept of expected value and how it can be used to solve probability problems effectively.

Defining Expected Value

Expected Value, also known as EV, is a statistical measure used to determine the long-term value of a variable. It is calculated by multiplying each possible outcome of a variable by its respective probability and then summing the results.

The formula for expected value can be represented as:

`Expected Value = (Outcome 1 * Probability 1) + (Outcome 2 * Probability 2) + … + (Outcome n * Probability n)`

Using Expected Value to Solve Probability Problems

Expected value is a useful tool for solving probability problems. By calculating the expected value of a variable, we can predict the long-term outcome of an unpredictable situation.

Here are some steps to follow when using expected value to solve probability problems:

  • Identify the variable of interest and all possible outcomes.
  • Assign probabilities to each possible outcome.
  • Calculate the expected value using the formula mentioned above.
  • Interpret the expected value to make decisions.

Common Probability Problems That Can Be Solved Using Expected Value

Expected value can be used to solve a wide range of probability problems. Here are some examples:

The Monty Hall problem

In this famous problem, a contestant is presented with three doors, behind one of which is a prize. After the contestant selects a door, the host opens a different door that doesn’t contain the prize.

The contestant is then given the option to switch to the remaining door or stick with their original choice. Using expected value, it is clear that the contestant should switch the door, as the probability of winning improves.

The dice game problem

In this problem, a player rolls two dice and wins if the total is seven, and loses otherwise.

The player bets $1 on each roll, but the payout on winning is $5. Again, using expected value, it is clear that the player has a positive expected value of $1.11 with this bet, so they should continue playing.

Practical Applications of Expected Value

Expected value also has practical applications in various fields such as gambling and insurance. Here are some examples:

Use of expected value in gambling

In gambling, expected value can help gamblers make informed decisions about which games to choose and which bets to place. By calculating the expected value of a bet, a gambler can tell whether the bet is worth making or not.

Use of expected value in insurance

Insurance companies use expected value to calculate premiums. By estimating how much they will have to pay out in claims, they can set premiums that ensure they make a profit in the long run.

Advantages and Disadvantages of Using Expected Value

Expected value can be a helpful tool for solving probability problems, but it also has some limitations. Here are some pros and cons of using expected value:

Pros of using expected value

  • Helps make informed decisions about uncertain events.
  • Provides a framework for evaluating risks.
  • Can be used to optimize decision-making.

Cons of using expected value

  • Can be difficult to calculate accurately.
  • Assumes a perfect understanding of the probability distribution.
  • Does not account for extreme outcomes or unlikely events.


Expected value is an essential concept in probability theory. By understanding how to calculate and use expected value, we can solve complex probability problems with ease. In order to become skilled at using expected value, it is important to practice and apply this concept to a wide range of problems.


Q.           What are the limitations of using expected value in probability problems?

Expected value assumes that the probability distribution is fixed and known, which may not be the case in all situations. Additionally, it does not account for extreme outcomes or unlikely events.

Q.           How is expected value related to variance in probability?

Variance measures how much a random variable deviates from its expected value. In other words, variance is a measure of volatility around the expected value.

Q.           Can expected value be negative?

Yes, expected value can be negative if the sum of the outcomes multiplied by their respective probabilities is negative. This indicates a net loss.

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