Understanding Probability of HIV and Herpes in Blood Donors

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Data from a blood center indicates that 0.1% of donors test positive for HIV, while 1% test positive for herpes, and 1.05% test positive for either HIV or herpes. The term "HIV or herpes" refers to the union of the two sets, confirming that the value for "HIV or herpes" is slightly higher than that for herpes alone. The probability formula P(A or B) = P(A) + P(B) - P(A and B) is applied, showing that the overlap, or co-infection rate, is 0.05%. This indicates a small percentage of donors are co-infected with both viruses. Understanding these probabilities is crucial for assessing risks associated with blood donations.
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Data gathered at a particular blood center show that 0.1% of all donors test positive for human immunodeficiency virus (HIV), 1% test positive for herpes, and 1.05% test positive for HIV or herpes.

Does the part in bold mean the union of HIV and herpes?
 
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Yes "A or B" means in set A or in set B and, yes, that is the union. Notice that the value for "HIV or Herpes" is slightly LARGER than for herpes only.
In general, P(A or B)= P(A)+ P(B)- P(A and B). Here, since 1.05= 0.1+ 1- 0.05 we can se that the test is 0.5 positive for "Herpes and HIV".
 
Halls, I know you meant 0.05 positive for "Herpes and HIV".
 
Thanks. Yes, of course, 0.05.
 
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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