What is the Paradox of Probability Density Functions?

In summary, the probability that a random variable X takes on any specific outcome is equal to 0, but the probability that X takes on any outcome between a and b is not 0. This is because when dealing with an uncountably infinite set of outcomes, such as an interval, the individual probabilities cannot be added together. This is similar to how the length of an interval is 1, even though each point in the interval has length 0.
  • #1
Peter G.
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Hi!

I am currently studying Probability Density Functions and I am having a hard time wrapping my head around something.

So, from what I have read, P(X=c), i.e. probability that the random variable X takes on any specific outcome, is equal to 0. Yet, the probability X takes on any outcome between a and b is not 0. Isn't the probability that X takes on any value between a and b equal to the probability X takes on each individual outcome between a and b added together? In other words, would not that be equal to summing several probabilities = 0?

I hope I made my doubt somewhat clear,

Thank you in advance!
 
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  • #2
Peter G. said:
So, from what I have read, P(X=c), i.e. probability that the random variable X takes on any specific outcome, is equal to 0. Yet, the probability X takes on any outcome between a and b is not 0. Isn't the probability that X takes on any value between a and b equal to the probability X takes on each individual outcome between a and b added together? In other words, would not that be equal to summing several probabilities = 0?
You are correct that if ##X## has a probability density function then ##P(X = c) = 0## for any specific outcome ##c##. Your confusion stems from the fact that if you consider an uncountably infinite set of outcomes, such as the interval ##[a,b]##, then you cannot simply add the probabilities of the individual points. This only works for a finite or countably infinite number of outcomes.

Forget about probability for a moment and consider the interval ##[0,1]##. This interval has length 1 even though each point in the interval has length zero. There's no contradiction here, it's just a fact of life: we can't add the lengths (or probabilities, or more generally, the measures) of an uncountably infinite number of objects.
 
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  • #3
Got it! Thank you very much jbunniii!
 

FAQ: What is the Paradox of Probability Density Functions?

What is a probability density function (PDF)?

A probability density function (PDF) is a mathematical function that describes the probability distribution of a continuous random variable. It is used to calculate the likelihood of a certain value occurring within a range of values.

How is a PDF different from a probability mass function (PMF)?

A probability mass function (PMF) is used to describe the probability distribution of a discrete random variable, while a PDF is used for continuous random variables. The PMF assigns probabilities to individual values, while the PDF assigns probabilities to ranges of values.

How is the area under a PDF curve related to probability?

The area under a PDF curve represents the probability of a random variable falling within a specific range of values. The total area under the curve is equal to 1, meaning that the probability of any value occurring within the entire range is 1.

What does the shape of a PDF tell us about the data?

The shape of a PDF can provide information about the distribution of the data. For example, a symmetric bell-shaped curve may indicate a normal distribution, while a skewed curve may indicate a non-normal distribution.

How can we use a PDF to make predictions?

A PDF can be used to calculate the probability of a random variable falling within a certain range of values, which can then be used to make predictions about future outcomes. It can also be used in statistical models to estimate the likelihood of certain events occurring.

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