# 12.2: Basic Concepts

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If you roll a die, pick a card from deck of playing cards, or randomly select a person and observe their hair color, we are executing an experiment or procedure. In probability, we look at the likelihood of different outcomes. We begin with some terminology.

The result of an experiment is called an **outcome**.

An **event** is any particular outcome or group of outcomes.

A **simple event **is an event that cannot be broken down further

The **sample space** is the set of all possible simple events.

If we roll a standard 6-sided die, describe the sample space and some simple events.

The sample space is the set of all possible simple events: \(\{1,2,3,4,5,6\}\)

Some examples of simple events:

We roll a 1

We roll a 5

Some compound events:

We roll a number bigger than 4

We roll an even number

Given that all outcomes are equally likely, we can compute the probability of an event \(E\) using this formula:

\(P(E)=\frac{\text { Number of outcomes corresponding to the event } \mathrm{E}}{\text { Total number of equally - likely outcomes }}\)

If we roll a 6-sided die, calculate

- P(rolling a 1)
- P(rolling a number bigger than 4)

**Solution**

Recall that the sample space is \(\{1,2,3,4,5,6\}\)

- There is one outcome corresponding to “rolling a 1”, so the probability is \(\frac{1}{6}\)
- There are two outcomes bigger than a 4, so the probability is \(\frac{2}{6}=\frac{1}{3}\)

Probabilities are essentially fractions, and can be reduced to lower terms like fractions.

Let's say you have a bag with 20 cherries, 14 sweet and 6 sour. If you pick a cherry at random, what is the probability that it will be sweet?

**Solution**

There are 20 possible cherries that could be picked, so the number of possible outcomes is 20. Of these 20 possible outcomes, 14 are favorable (sweet), so the probability that the cherry will be sweet is \(\frac{14}{20}=\frac{7}{10}\).

There is one potential complication to this example, however. It must be assumed that the probability of picking any of the cherries is the same as the probability of picking any other. This wouldn't be true if (let us imagine) the sweet cherries are smaller than the sour ones. (The sour cherries would come to hand more readily when you sampled from the bag.) Let us keep in mind, therefore, that when we assess probabilities in terms of the ratio of favorable to all potential cases, we rely heavily on the assumption of equal probability for all outcomes.

At some random moment, you look at your clock and note the minutes reading.

- What is probability the minutes reading is 15?
- What is the probability the minutes reading is 15 or less?

**Answer**-
There are 60 possible readings, from 00 to 59.

- \(\frac{1}{60}\)
- \(\frac{16}{60}\) (counting 00 through 15)

A standard deck of 52 playing cards consists of four **suits** (hearts, spades, diamonds and clubs). Spades and clubs are black while hearts and diamonds are red. Each suit contains 13 cards, each of a different **rank**: an Ace (which in many games functions as both a low card and a high card), cards numbered 2 through 10, a Jack, a Queen and a King.

Compute the probability of randomly drawing one card from a deck and getting an Ace.

**Solution**

There are 52 cards in the deck and 4 Aces so

\[P(A c e)=\dfrac{4}{52}=\frac{1}{13} \approx 0.0769 \nonumber\]

We can also think of probabilities as percents: There is a 7.69% chance that a randomly selected card will be an Ace.

Notice that the smallest possible probability is 0 – if there are no outcomes that correspond with the event. The largest possible probability is 1 – if all possible outcomes correspond with the event.

An **impossible event** has a probability of 0.

A **certain event** has a probability of 1.

The probability of any event must be \(0 \leq P(E) \leq 1\).

In the course of this chapter, *if you compute a probability and get an answer that is negative or greater than 1, you have made a mistake and should check your work*.