Undergrad Equivalent definitions of random variable

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A random variable can be described by its cumulative distribution function (CDF), but this does not mean it can be defined solely as a subset of ℝ associated with a CDF. Multiple distinct random variables can share the same CDF while being defined on different probability spaces, which highlights the importance of their functional definition. For example, two questionnaires measuring different interests can yield random variables with identical distributions but are fundamentally different due to their distinct contexts. The current Wikipedia article on random variables contains contradictions and misleading statements regarding cumulative distributions. A more precise understanding of random variables is typically developed in advanced probability courses.
mnb96
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Hello,

According to the Wikipedia article on random variables:
"Any random variable can be described by its cumulative distribution function, which describes the probability that the random variable will be less than or equal to a certain value."
If the above statement is true, then, instead of defining a (real) random variable as a function from a sample space of some probability space to the reals, could we equivalently define it as a subset of ℝ associated with a CDF?
 
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No. To one and the same CDF, there might be multiple very different associated random variables. Sure, these random variables will have the same probability distribution, but they're not the same as function.
 
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mnb96 said:

In its current state, that article has some self-contradictions. The abstract definition it gives for random variable looks correct. The statements it makes about cumulative distributions are misleading.

Consider the following situation. We have two questionnaires. Questionnaire A is designed to rate a persons interest in applied mathematics "on a scale" from 0 to 10. Questionnaire B is designed to rate a person's interest in basketball on a scale from 0 to 10.

A realization of Random variable X is defined as "Pick a student at your school at random and administer questionnaire A. Let X be the student's score on the questionnaire. A realization of Random variable Y is defined as "Pick a student at you school at random and administer questionnaire B. Let Y be the student's score on questionnaire B.

It is possible that random variable X and random variable Y might have the same distribution and same cumulative distribution. But the two random variables are not the same random variable because they are not defined on the same probability space. X is defined on a space of events having to do with questionnaire A while Y is defined on a space of events having to do with questionnaire B.
 
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Hey mnb96.

A graduate course in probability will make this definition more precise.

The idea is that you have a sigma algebra, a Borel space and you mix the two up to make the idea of a probability space [with its events and actual probabilities] consistent with what a probability space actually is.
 
OK.
Thank you all. I think all the answers I received were pretty clear.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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