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I am comfortable with explaining to my high school students that for an event $A$ we have that

$P(A) + P(A^C) = 1$

But what is the best way to help students realize that

$P(A \mid B) + P(A^C \mid B) = 1$

remains true when you have the additional assumption of the event $B$ occuring. To be clear; I understand why this is so, I am just afraid of my explanation being too technical.

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    $\begingroup$ Just say that conditional probability is still a probability so inherits the properties. In practice, all probabilities are conditional. When throwing a dice on a pier, the probabilities are conditional on the dice not disappearing in the water, ... $\endgroup$ – kjetil b halvorsen Apr 20 '17 at 9:25
  • $\begingroup$ As a student, I think I would respond well to drawing some tree diagrams and talking each case through with the logic of Mikhail Katz' answer. Perhaps someone with proper teaching experience has found success with this and can post an answer along these lines. $\endgroup$ – Will R Apr 22 '17 at 10:52
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The key lies in reiterating the definition of probability as $$\frac{\rm favorable {\ } cases}{\rm possible {\ } cases}$$ The conditional probabilities are computed with less possible cases.

For example, say we roll two dice, one after the other (so we can differentiate them), and consider the following statements:

A: "The second die is bigger than or equal to the first"

B: "The sum of rolls is 6"

To compute $P(A)$ and $P(A^c)$, we consider 36 possible cases, with 21 and 15 favorable cases respectively.

But the conditional probabilities $P(A|B)$ and $P(A^c|B)$ only consider as possible those cases in which $B$ occurred, namely 5. Of these (and only these) cases, 3 are favorable to statement $A$, and 2 to its negation.

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If your mother gives you a candy, either you will eat it or you won't. Sure thing.

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You can realize probability as the relative area between a figure and an interesting part of that figure.

For instance, if you have a calendar of last year, and you marked all the days where it rained in blue, the probability during the year that it will rain is defined as the number of blue days to the number of days total

But different months rain different amounts. If we look only at the month of April, likely more will be blue that month than on any other month.

Conditional probability here would be linked to looking only at the month of April.

The total probability, then, is the number of days it rained (days marked in blue) and the number of days it didn't (not marked) added together, divided by the number of total days. Of course, there are 30 days in April, and hopefully each day, it either rained or it didn't rain, since any other possibility would be hard to imagine.

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