Discrete random variable cumulative distrub. function

AI Thread Summary
The discussion revolves around the cumulative distribution function (CDF) of a discrete random variable, specifically questioning why F(3) equals 1 if F(>3) also equals 1. Participants clarify that if F(>3) = 1, it implies that all probabilities are accounted for at or below 3, leading to F(3) also being 1. The conversation emphasizes understanding the properties of the CDF in relation to discrete random variables. The key takeaway is that for a CDF, if the probability of exceeding a certain value is 1, then the CDF at that value must also be 1. Understanding these fundamental concepts is essential in probability theory.
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What do you mean, why? It seems straightforward. Do you mean why as in, why does 1 = 1? Why does F(3) = 1? Or don't you think it is 1.

What do you think F(3) is or should be?
 

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