This document discusses basic concepts of probability, including: - The addition rule and multiplication rule for calculating probabilities of compound events. - Events can be disjoint (mutually exclusive) or not disjoint. - The probability of an event occurring or its complement must equal 1. - How to calculate the probability of at least one occurrence of an event using the complement. - When applying the multiplication rule, you must consider whether events are independent or dependent.

1. BASIC CONCEPTS OF
PROBABILITY
Avjinder Singh Kaler and Kristi Mai

2.  Basic Concepts of Probability
 Addition Rule
 Multiplication Rule: Basics

3.  If, under a given assumption, the probability of a particular observed event is
extremely small, we conclude that the assumption is probably not correct.
 Example:
• You go into a room and watch a man toss a coin 20 times, getting 20 heads in a row.
• Assumption: the coin is a normal one. (This would be the normal assumption made
when you first walked into the room.)
• If the assumption is true, you have witnessed an incredibly unlikely event. According
to the rare event rule, therefore, what you have witnessed is evidence against the
assumption: on this basis, you conclude the assumption is wrong (the coin probably
has heads on both sides.)

4.  Event: any collection of results or outcomes of a procedure
 Simple Event: an outcome or an event that cannot be further broken
down into simpler components
 Sample Space: for a procedure consists of all possible simple events;
that is, the sample space consists of all outcomes that cannot be
broken down any further

5. In the following table, we use “b” to denote a baby boy and “g” to denote a
baby girl.
Procedure Example of Event Sample Space
Single birth 1 girl (simple event) {b, g}
3 births 2 boys and 1 girl
(bbg, bgb, and gbb are all simple events)
{bbb, bbg, bgb, bgg, gbb, gbg, ggb, ggg}

6. P - denotes a probability.
A, B, and C - denote specific events.
P(A) - denotes the probability of event A occurring.

7. Rule 1: Relative Frequency Approximation of Probability
Conduct (or observe) a procedure, and count the number of times event A
actually occurs. Based on these actual results, P(A) is approximated as
follows:
P(A) =
# of times A occurred
# of times procedure was repeated

8. Example: Relative Frequency Probability (Smoking)
A recent Harris Interactive survey of 1010 adults in the United States showed
that 202 of them smoke. Find the probability that a randomly selected adult in
the United States is a smoker.
Solution:
𝑃 𝑠𝑚𝑜𝑘𝑒𝑟 =
𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑚𝑜𝑘𝑒𝑟𝑠
𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑒𝑜𝑝𝑙𝑒 𝑠𝑢𝑟𝑣𝑒𝑦𝑒𝑑
=
202
1010
= 0.200
Note: The classical approach cannot be used since the two outcomes
(smoker, not a smoker) are not equally likely

9. Rule 2: Classical Approach to Probability (Requires Equally Likely Outcomes)
Assume that a given procedure has n different simple events and that each
of those simple events has an equal chance of occurring. If event A can
occur in s of these n ways, then
s A
P A
n
number of ways can occur
( ) = =
number of different simple events

10. Example:
When three children are born, the sample space is:
{bbb, bbg, bgb, bgg, gbb, gbg, ggb, ggg}
Assuming that boys and girls are equally likely, find the probability of getting
three children of all the same gender.
Solution:
 
2
three children of the same gender 0.25
8
P  

11.  As a procedure is repeated again and again, the relative frequency
probability of an event tends to approach the actual probability.
Example:
For example, a single roll of a fair, six-sided dice produces one of the numbers
1, 2, 3, 4, 5, or 6, each with equal probability. Therefore, the expected value of
a single die roll is
1 + 2 + 3 + 4 + 5 + 6
6
=
21
6
= 3.5
According to the law of large numbers, if a large number of six-sided dice are
rolled, the average of their values (sometimes called the sample mean) is likely
to be close to 3.5, with the precision increasing as more dice are rolled.

13.  Always express a probability as a fraction or decimal number
between 0 and 1.
 The probability of an impossible event is 0, that is,
𝑷 𝒊𝒎𝒑𝒐𝒔𝒔𝒊𝒃𝒍𝒆 𝒆𝒗𝒆𝒏𝒕 = 𝟎
 The probability of an event that is certain to occur is 1, that is,
𝑷 𝒄𝒆𝒓𝒕𝒂𝒊𝒏 𝒆𝒗𝒆𝒏𝒕 = 𝟏
 For any event A, the probability of A is between 0 and 1 inclusive.
That is, 𝟎 ≤ 𝑷(𝒂𝒏𝒚 𝒆𝒗𝒆𝒏𝒕) ≤ 𝟏.

15.  The complement of event A, denoted by 𝑨, consists of all outcomes in
which the event A does not occur.
Example:
1010 United States adults were surveyed and 202 of them were smokers.
It follows that the other 808 are not smokers, so
 
 
202
smoker 0.200
1010
202
not a smoker 1 0.800
1010
 
  
P
P

16.  Any event combining 2 or more simple events
Notation
P(A or B) = P(in a single trial, event A occurs or event B occurs or
they both occur)
General Rule for a Compound Event:
When finding the probability that event A occurs or event B occurs, find the
total number of ways A can occur and the number of ways B can occur, but
find that total in such a way that no outcome is counted more than once.
P(A or B) is equal to that sum, divided by the total number of outcomes in the
sample space.

17. Formal Addition Rule
P(A or B) = P(A) + P(B) – P(A and B)
where P(A and B) denotes the probability that A and B both occur at
the same time as an outcome in a trial of a procedure.

18. Example:
Refer to Table 4-1. If 1 subject is randomly selected from 1000 subjects given a drug test, find the
probability of selecting a subject who had a positive test result or uses drugs. This probability is
denoted by 𝑃 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑡𝑒𝑠𝑡 𝑟𝑒𝑠𝑢𝑙𝑡 𝑜𝑟 𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑢𝑠𝑒𝑠 𝑑𝑟𝑢𝑔𝑠 .
Solution:
𝑃 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑡𝑒𝑠𝑡 𝑟𝑒𝑠𝑢𝑙𝑡 𝑜𝑟 𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑢𝑠𝑒𝑠 𝑑𝑟𝑢𝑔𝑠 =
140
1000
= 0.140
Table 4-1 Pre-Employment Drug Screening Results
Positive Test Result Negative Test Result
Subject Uses Drugs 44 (True Positive) 6 (False Negative)
Subject Is Not a Drug User 90 (False Positive) 860 (True Negative)

19.  Events A and B are disjoint (or mutually exclusive) if they cannot occur
at the same time. (That is, disjoint events do not overlap.)
Venn Diagram for Events That Are
Not Disjoint
Venn Diagram for Disjoint Events

20. A and 𝐴 must be disjoint.
It is impossible for an event and its complement to
occur at the same time.

21. ( ) ( ) 1P A P A 
( ) 1 ( )P A P A 
( ) 1 ( )P A P A 

22.  “At least one” is equivalent to “one or more.”
 The complement of getting at least one particular event is that you get no
occurrences of that event.
 Backward method of finding the probability of getting at least one of some event:
1. Let 𝐴 = 𝑔𝑒𝑡𝑡𝑖𝑛𝑔 𝑎𝑡 𝑙𝑒𝑎𝑠𝑡 𝑜𝑛𝑒 𝑜𝑓 𝑠𝑜𝑚𝑒 𝑒𝑣𝑒𝑛𝑡
2. Then 𝐴 = 𝑔𝑒𝑡𝑡𝑖𝑛𝑔 𝑛𝑜𝑛𝑒 𝑜𝑓 𝑡ℎ𝑒 𝑒𝑣𝑒𝑛𝑡 𝑏𝑒𝑖𝑛𝑔 𝑐𝑜𝑛𝑠𝑖𝑑𝑒𝑟𝑒𝑑
3. Find 𝑃 𝐴 = 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑡ℎ𝑎𝑡 𝑒𝑣𝑒𝑛𝑡 𝐴 𝑑𝑜𝑒𝑠 𝑛𝑜𝑡 𝑜𝑐𝑐𝑢𝑟
4. Subtract the result from 1. That is, evaluate this expression:
𝑃 𝑎𝑡 𝑙𝑒𝑎𝑠𝑡 𝑜𝑛𝑒 𝑜𝑐𝑐𝑢𝑟𝑟𝑒𝑛𝑐𝑒 𝑜𝑓 𝑒𝑣𝑒𝑛𝑡 𝐴 = 1 − 𝑃(𝑛𝑜 𝑜𝑐𝑐𝑢𝑟𝑟𝑒𝑛𝑐𝑒𝑠 𝑜𝑓 𝑒𝑣𝑒𝑛𝑡 𝐴)

23.  To find the probability of at least one of something, calculate the
probability of none and then subtract that result from 1.
 That is, P(at least one) = 1 – P(none).

24. Topford supplies X-Data DVDs in lots of 50, and they have a reported
defect rate of 0.5% so the probability of a disk being defective is 0.005.
It follows that the probability of a disk being good is 0.995. What is the
probability of getting at least one defective disk in a lot of 50?
Solution:
Step 1: Let A = at least 1 of the 50 disks is defective.
Step 2: Identify the event that is the complement of A.
𝐴 = 𝑛𝑜𝑡 𝑔𝑒𝑡𝑡𝑖𝑛𝑔 𝑎𝑡 𝑙𝑒𝑎𝑠𝑡 1 𝑑𝑒𝑓𝑒𝑐𝑡𝑖𝑣𝑒 𝑑𝑖𝑠𝑘 𝑎𝑚𝑜𝑛𝑔 50 = 𝑎𝑙𝑙 50 𝑑𝑖𝑠𝑘𝑠 𝑎𝑟𝑒 𝑔𝑜𝑜𝑑
Step 3: Find the probability of the complement by evaluating 𝑃 𝐴 .
𝑃 𝐴 = 𝑃 𝑎𝑙𝑙 50 𝑑𝑖𝑠𝑘𝑠 𝑎𝑟𝑒 𝑔𝑜𝑜𝑑 = 0.995 ∗ 0.995 ∗ ⋯ ∗ 0.995 = 0.99550 = 0.778
Step 4: Find 𝑃(𝐴) by evaluating 1 − 𝑃(𝐴).
𝑃 𝐴 = 1 − 𝑃 𝐴 = 1 − 0.778 = 0.222

25.  The basic multiplication rule is used for finding P(A and B), the
probability that event A occurs in a first trial and event B occurs in a
second trial.
 If the outcome of the first event A somehow affects the probability of
the second event B, it is important to adjust the probability of B to
reflect the occurrence of event A.

26. P(A and B) = P(event A occurs in a first trial and
event B occurs in a second trial)
P(B | A) represents the probability of event B
occurring after event A has already occurred.

27. When finding the probability that event A occurs in one trial and event B
occurs in the next trial, multiply the probability of event A by the probability of
event B, but be sure that the probability of event B takes into account the
previous occurrence of event A.
( and ) ( ) ( | ) P A B P A P B A

28. When applying the multiplication rule, always consider whether the
events are independent or dependent, and adjust the calculations
accordingly.

29.  Two events, A and B, are independent if the occurrence of one does not
affect the probability of the occurrence of the other.
 Two events, A and B, are dependent if the occurrence of one of them
affects the probability of the occurrence of the other, but this does not
necessarily mean that one of the events is a cause of the other.

30.  Sampling with replacement:
• Selections are independent events
 Sampling without replacement:
• Selections are dependent events

31. In general, the probability of any sequence of independent events is
simply the product of their corresponding probabilities.
Recall: 𝑃 𝐴 𝑎𝑛𝑑 𝐵 = 𝑃 𝐴 ∗ 𝑃 𝐵 𝐴
If A and B are independent then 𝑃 𝐵 𝐴 = 𝑃 𝐵
So then, 𝑃 𝐴 𝑎𝑛𝑑 𝐵 = 𝑃 𝐴 ∗ 𝑃 𝐵

32. Suppose 50 drug test results are given from people who use drugs:
a) If 2 of the 50 subjects are randomly selected with replacement, find the
probability that the first selected person had a positive test result and the
second selected person had a negative test result.
a) a) If 2 of the 50 subjects are randomly selected without replacement, find
the probability that the first selected person had a positive test result and
the second selected person had a negative test result.
Positive Test Results: 44
Negative Test Results: 6
Total Results: 50

33. a) With replacement:
• First selection: 𝑃 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑡𝑒𝑠𝑡 𝑟𝑒𝑠𝑢𝑙𝑡 =
44
50
• Second selection: 𝑃 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑡𝑒𝑠𝑡 𝑟𝑒𝑠𝑢𝑙𝑡 =
6
50
• We now apply the multiplication rule:
𝑃 1𝑠𝑡 𝑠𝑒𝑙𝑒𝑐𝑡𝑖𝑜𝑛 𝑖𝑠 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑎𝑛𝑑 2𝑛𝑑 𝑖𝑠 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 =
44
50
∗
6
50
= 0.106
b) Without replacement:
• First selection: 𝑃 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑡𝑒𝑠𝑡 𝑟𝑒𝑠𝑢𝑙𝑡 =
44
50
• Second selection: 𝑃 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑡𝑒𝑠𝑡 𝑟𝑒𝑠𝑢𝑙𝑡 =
6
49
• We now apply the multiplication rule:
𝑃 1𝑠𝑡 𝑠𝑒𝑙𝑒𝑐𝑡𝑖𝑜𝑛 𝑖𝑠 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑎𝑛𝑑 2𝑛𝑑 𝑖𝑠 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 =
44
50
∗
6
49
= 0.108

34.  In the addition rule, the word “or” in P(A or B) suggests addition. Add
P(A) and P(B), being careful to add in such a way that every outcome
is counted only once.
 In the multiplication rule, the word “and” in P(A and B) suggests
multiplication. Multiply P(A) and P(B), but be sure that the probability
of event B takes into account the previous occurrence of event A.