What distribution should i use?

In summary, the players are discussing a bet where only one player can win. The first player is interested in knowing the expected number of players that will accept the bet. They assume that the chance of a player accepting the bet is not affected by the number of players who have already accepted it. The average number of players accepting the bet is 32% + 56% + 20%.
  • #1
rsala004
23
0
Player1 makes the bet.
the 2nd player has a 32% chance to accept the bet.
the 3rd player has a 56% chance to accept the bet.
the 4th player has a 20% chance to accept the bet.

there can only be one winner of said bet, so player one is interested in knowing the EXPECTED number of players that will call his bet (how much competition will he have)

(we can make the assumption that the % chance of a player accepting the bet does not change depending on the # of players accepted before him..they aren't that smart)

anyone have an idea how I can approach this?

haven't taken stats in a while
 
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  • #2
The question doesn't seem to be clear . I assume there are only 2 outcomes to a bet - win or lose . Now you say that no more than 1 player can win the bet , but how can that be :
If player 1 makes the bet , and then player 2 and player 3 accepts the bet , but player 4 declines .

If player 1 loses the bet then that means player 2 and 3 have won . But you said that no more than 1 player can win the bet. How come ?
 
  • #3
The average number of players accepting the bet is 32% + 56% + 20%, of course.
 
  • #4
CRGreathouse said:
The average number of players accepting the bet is 32% + 56% + 20%, of course.

lol so obvious i didnt notice.
 
  • #5
rsala004 said:
lol so obvious i didnt notice.

...and that's what we're here for.
 

FAQ: What distribution should i use?

What is the purpose of using a distribution in statistical analysis?

The purpose of using a distribution in statistical analysis is to understand and describe the pattern of data and to make predictions based on the collected data. Distributions can help determine the likelihood of certain events occurring and provide a framework for making inferences about a population based on a sample.

How do I know which distribution to use for my data?

The distribution to use for your data depends on the type of data you have and the research question you are trying to answer. For continuous data, such as measurements, the normal distribution is often used. For categorical data, such as survey responses, the binomial or categorical distribution may be appropriate. It is important to consider the characteristics of your data and the assumptions of each distribution before deciding which one to use.

Can I use more than one distribution for my data?

Yes, it is possible to use multiple distributions for your data. This is often seen in more complex statistical analyses, such as regression models, where different distributions are used for different predictors. However, it is important to carefully consider the assumptions and limitations of each distribution and to ensure that they are appropriate for your data and research question.

How do I determine if my data follows a particular distribution?

There are various statistical tests and techniques that can be used to assess whether your data follows a particular distribution. These include visual methods, such as histograms and Q-Q plots, as well as statistical tests, such as the Kolmogorov-Smirnov test. However, it is important to keep in mind that these tests have limitations and should not be the sole basis for choosing a distribution for your data.

What should I do if my data does not follow a known distribution?

If your data does not follow a known distribution, you may need to consider using a different type of statistical analysis or transforming your data to better fit a distribution. You could also consult with a statistician or conduct further research to see if there are other distributions that may be more appropriate for your data. Additionally, it is important to carefully consider the assumptions and limitations of the distribution you choose and to interpret your results with caution.

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