Solving Probability Question: Expected Outcomes vs Total Possible & Event Type

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The probability of drawing a red card from a standard deck is 1/2, with 26 favorable outcomes out of 52 total possible outcomes. The expected outcome for drawing a red card is calculated as E(x) = n*p, resulting in an expected value of 1/2. This scenario represents a binomial distribution, where there are two possible outcomes: success (drawing a red card) or failure (drawing a non-red card). Clarification on the wording of the problem suggests it may be asking for the number of favorable outcomes specifically. Understanding the classification of events in probability is also recommended for further insight.
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I'm trying to solve a problem that states: Select a card from the deck. What is the probability that this card will be red? Show the number of expected outcomes versus the number of total possible outcomes. What type of event does this represent.

I know (at least I think I know) that the probability of the card being red is 1/2. But what is the expected outcome vs total possible? And what is the event type?

Any help with this question would be greatly appreciated! Thanks!
 
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Something is wrong with the wording there. You expect 1 outcome, 1 out of a possible 52 outcomes. They might mean for the number of outcomes where the card is red. Clearly, this is 26. As for "what type of event..." you should probably look in your book, maybe they've given some definitions or classifications for types of events, and they want you to categorize this event as one of those.
 
This is a binomial distribution. There are two outcomes, success or failure.

Expected Value = E(x) = n*p = 1*1/2 = 1/2
 
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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