chapter 4- Example of Drawing a Card

Scenario-3:

Suppose that you have with you a standard deck of  cards, and you shuffle it really well. You now draw a card at random from this deck. It is obvious that for a well-shuffled deck, you are equally likely to draw any of the  cards. For example, if  is the event that the card drawn is the king of Hearts while  is the event that the card drawn is the seven of Diamonds, we have

Since there are  cards and each is equally likely to turn up, and also, one of the  cards must turn up, the numerical value of the probability of any card being drawn is therefore . For example,

Now, let  be the event that a spades is drawn. Since  can occur in  ways (there are  cards in any suit), or in other words, the number of cases favorable to  is  out of the total  possibilities, we must have

This value of  should have been obvious directly, since there are only suits in the deck, and any suit is equally likely to turn up.

Going further, let  be the event that a black card is drawn. We have (represents ‘Number of’),

We see that , since there are  cases favorable to . Again, the value of  should have been obvious directly, since there are only colors in the deck (red, black), and each color is equally likely to turn up.

From this discussion, you should by now have understood the gist of the concept of probability. We can, taking cue from the preceeding examples, define the probability of an event as the number of outcomes favorable to that event, divided by the total number of possible outcomes (assuming each outcome to be equally likely).

Extending this further, the general idea is that if we have two events  and with no outcomes in common, the first consisting of  outcomes and the second of  outcomes, then the event  which consists of all the outcomes in  and  consists of  outcomes, i.e,

(if  and  have no outcomes in common)

An example of this is what we just saw:

Thus, if the total number of outcomes is ,

we have,

That the probabilities add when we consider the event  consisting of all outcomes in  and  is justified only if  and  have no outcomes in common, as we’ve been seeing till now.

What about the probability of an event consisting of all outcomes in two events and  which may have some outcomes in common? For example, let  and  be events defined as follows:

	: The card drawn is even
	: The card drawn is black

The number of outcomes in  is  (why ? ). The number of outcomes in  is . However, if we now consider the event  which consists of all the outcomes in  and , we find that the relation

is not satisfied. Why? Because the events  and  have some outcomes in common. Let us understand this visually.

We let the large rectangle below represent all the  possible outcomes. The events  and  are then subsets of this large rectangle as shown.

Note that  and  have some elements in common, which is technically stated by saying that  and  are not mutually exclusive. (Thus, mutually exclusive events have no outcomes in common).

As stated earlier, the event  is defined to consist of all the outcomes in  and , which (in easier language!) means that  is the event that the card drawn is either even or black (or both). How many elements are there in ?

Note that when we write

the outcomes common to  and  (represented by the dark regions in Figure –) are counted twice on the right side. The actual number of outcomes in  will thus be obtained by subtracting from the  the number of these common outcomes, and thus we can write

where  represents the number of common outcomes. In this case, would represent the event that the card drawn is both even and black. There are obviously  such cards, as is evident from Figure – , and thus,

You are urged to verify this result by explicitly counting the number of cards lying within the (total) shaded regions in Figure – .

In passing, it should be mentioned that the event  which has been defined as the event consisting of all the outcomes in  and  is technically termed as the union of the events  and , and this fact is symbolically written as

Union of events

You can think of the union of two events to be the event which can be said to occur when either of the two given events (or both) occurs.

Analogously, the event  defined to consist of all outcomes common to  and is technically termed as the intersection of the events  and , and this fact is symbolically written as

Intersection of events

You can think of the intersection of two events to be the event which can be said to occur when both the two given events occur simultaneously. Finally, as we’ve seen above, we always have

If  and  have no outcomes in common, that is, if  and  are mutually exclusive:

this relation reduces to

Here’s a visual depiction to help you get things straight:

The relations  and , when written in terms of probability (by dividing both sides with the total number of outcomes), become,

		General relation
		The particular case of and  being mutually exclusive

If the concept of mutually exclusive events is now clear to you, the significance of the relation  on Page –  immediately becomes apparent, where we simply summed the six probabilities and wrote the sum as . All the six outcomes are mutually exclusive, and they exhaust all possibilities, so  is justified.

Now is a good point to learn some more terminology. In Figure –  above, each of the six possible outcomes is the most elementary outcome possible. For example, if  is the event of a  showing up, the event  consists of only one outcome, the number .  would therefore be called an elementary event. Now consider the event  of an odd number showing up.  consists of three outcomes, namely ,  and .  would therefore be termed a compound event.

As a further example, in the drawing of a card at random from a standard deck of  cards, let  and  be events defined as follows:

	: The card drawn is the king of hearts
	: The card drawn is a Hearts

 is an elementary event since  consists of only one possible outcome, the king of Hearts (stated differently, ). However,  is a compound event since  consists of  possible outcomes, namely the  cards of Hearts. In other words, 

In the experiment of drawing one card at random from a standard well-shuffled deck of  cards, consider the event  defined as:

: The card drawn is a king

Obviously, we have

However, if you are now asked to find the probability of event  occurring given that the card drawn has a number greater than , what would you say?

To answer correctly, you must understand that now the situation is entirely different from earlier. Now we already know that the card drawn has a number greater than , which means that the number of possibilities for the card has reduced; earlier, there were  possibilities, but now the number of possible cards are the  Jacks, the  Queens and the  Kings, which means that now there are only  possibilities for the card. Of these, the favorable possibilities are  ; thus,

Let us denote by  the event that the card drawn has a number greater than . The probability just calculated above is then written in standard notation as

which is read as

i.e. “the probability of event  occurring given that  has occurred”.

Let us consider another example. In the random experiment of throwing two dice, let events  and  be defined as

	: The sum of the two numbers on top is
	: One of the numbers on top is

First, let us find the probability of  occurring. This is simply

Now, suppose we are given that  has already occurred, meaning we now know that one of the numbers on top is . What is the probability of  now when we already possess this information?

The number of total possibilities now are ; we list them explicitly:

Out of these, the favorable possibilities are only , namely:  and . Thus

In both the preceding examples, what happens is that with the possession of some information, the number of possibilities reduce, i.e., the sample space reduces. In the first example, with the information that  has occurred, the number of possibilities reduces from  to . We then looked for favorable cases only within this reduced sample space of  outcomes. Similarly, in the second example, when we possess the information that  has occurred, the number of possibilities reduces from  to ; it is in this reduced sample space of outcomes that we then looked for the favorable possibilities.

In passing we should mention that probabilities of the form  are called conditional probabilities.  is said to be the conditional probability of  given that  has occurred.

Let us understand all this somewhat more deeply. In particular, let us ponder over the following question: Let  and  be two events. Let the probability of  occurring be . Now, if we come to know that  has occurred, will the probability of  occurring always increase, like it did in the last two examples, or can it decrease too? Can it remain the same?

It turns out that all the three are possible, which should even be obvious to an alert reader.

Let us consider the case where the information that  has occurred decreases the probability of  occurring. Let  and  be, in the card drawing experiment of the first example

	: The card drawn has a number which is a multiple of four
	: The card drawn has a number greater than eight

The original probability of  occuring is

But when  has already occurred, the probability of  occurring is

which is lesser than the original probability of  occurring. This should be intuitively obvious: In the first case, there are  multiples of four per suit of cards. In the second, when we are told that the number of the card is greater than eight, the multiple of four possible is only  per suit which is the queen. Thus the favorable possibilities decrease to one-third. The total possibilities also decrease i.e, from  to  per suit, but it can be easily appreciated that the percentage reduction in favorable possibilities is greater than the percentage reduction in total possibilities.

We now come to the third very important question: for two events  and , can  be the same as ? You must appreciate that this is equivalent to saying that the probability of  occurring is not affected by the occurrence or non-occurrence of .

As we said earlier, this is possible, and such events are called independent events:

	If
	and  are independent events

Let us see an example. In the card-drawing experiment, let events  and  be defined as

	: The card has a number greater than
	: The card is black

The alert reader will immediately realize that  is the same as . Why? Because, the knowledge that the card is black does not change the number of cards per suit that are greater than eight. Stated explicitly,

	and	because there are only two black suits

There is one important point the reader must notice and appreciate:

If 

	this means that  and  are independent events
	this should also mean that  should be the same as

Let us verify this in the example above. We have,

and,

which confirms the assertion

Note that the favorable cases while calculating  are those cases in that are common to ; the total cases are all cases in . Thus

If the entire sample space of the experiment consists of  out comes, we can write  as

Similarly,

This means that if  and  are independent, then

		From  and

Thus, the probability of the intersection of two independent events is simply the product of the individual probabilities.