[sjones171]

I am trying to qualify uniform probability models with my colleagues and I need to clarify some of your discussion last year with SteveG. The reason that selecting a student is a uniform probability model is because each of the 10 students (the outcomes) has an equal chance of being selected, correct?

In SteveG’s question, he moved from the experiment (selecting a student) to a specific event – select a 7th grader (a subset of the sample space). The selection of a student is equally likely, but the selection of a 7th grader is not equally likely (0.4 to 0.6), but each student is still equally likely to be chosen.

Let’s say we have a spinner divided into equal fifths. If the sections were labeled 1 to 5, then the spinner would be a uniform probability model because the sample space is {1, 2, 3, 4, 5} and each outcome is equally likely to happen. Spinning an odd number would be the sum of all the probabilities of the odd numbers = 1/5 +1/5 + 1/5 = 3/5. Just like above, the spinner is equally likely to land on any number on the spinner.

Taking this one step further, take the same spinner, but label two sections “1” and three sections “2.” This is no longer a uniform probability distribution because the sample space is now {1, 2}, but P(1) = 2/5 and P(2) = 3/5. The outcomes no longer are equally likely to occur. Would you agree with this?

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