Chapter 6 Probability. Inferential Statistics Samples - so far we have been concerned about describing and summarizing samples or subsets of a population.

Chapter 6

Probability

Inferential StatisticsSamples - so far we have been

concerned about describing and summarizing samples or subsets of a population

Inferential stats allows us to “go beyond” our sample and make educated guesses about the population

Inferential Statistics

But, we need some help which comes from Probability theory

Inferential = Descriptive + Probability

Statistics Statistics Theory

What is probability theory and what is its role?

Probability theory, or better “probability theories” are found in mathematics and are interested in questions about unpredictable events

Although there is no universal agreement about what probability is, probability helps us with the possible outcomes of random sampling from populations

Examples “The Chance of Rain” The odds of rolling a five at the craps table (or

winning at black jack) The chance that a radioactive mass will emit a

particle The probability that a coin will come up heads

upon flipping it* The probability of getting exactly 2 heads out of 3

flips of a coin* The probability of getting 2 or more heads out of

3 flips*

Set Theory (a brief digression)Experiment - an act which leads to an

unpredictable, but measurable outcome

Set - a collection of outcomesEvent - one possible outcome; a value

of a variable being measuredSimple probability – the likelihood that

an event occurs in a single random observation

Simple ProbabilitiesTo compute a simple probability (read

the probability of some event), p(event):

Probability TheoryMost of us understand probability in

terms of a relative frequency measure (remember this, f/n), the frequency of occurrences (f) divided by the total number of trials or observations (n)

However, probability theories are about the properties of probability not whether they are true or not (the determination of a probability can come from a variety of sources)

Example

100 marbles are Placedin a jar

Relative Frequency (a reminder)

What if we counted all of the marbles in the jar and constructed a frequency distribution?

We find 50 black marbles, 25 red marbles, and 5 white marbles

Relative frequency (proportion) seems like probability

Color f rf

Black 50.50

Red 25.25

White 25 .25Total 100

1.00

Relative Frequency and Probability Distributions A graphical

representation of a relative frequency distribution is also similar to a “Probability Distribution”

00.10.20.30.40.50.60.70.80.9

1

Rel

ativ

e F

req

uen

cy/

Pro

bab

ilit

y

Black

White

Red

What does this mean?

What will happen if we choose a single marble out of the jar?

If we chose 100 marbles from the jar, tallied the color, and replaced them, will we get 50, 25, and 25? If so, what if we selected only 99?

If .5, .25, and .25 are the “real” probabilities, then “in the long run” will should get relative proportions that are close to .5, .25, and .25

Bernoulli’s TheoremThe notion of “in the long run” is

attributed to Bernoulli It is also known as the “law of large

numbers”as the number of times an experiment

is performed approaches infinity (becomes large), the “true” probability of any outcome equals the relative proportion

Venn Diagrams

SA

A

Venn Diagrams

S “all the marbles”

A“Red”

A“not red”

SA

Mutually Exclusive Events

B

Mutually Exclusive Events

Axiom’s of Probability

1.The probability of any event A, denoted p(A), is 0 < p(A) < 1

2.The probability of S, or of an event in sample space S is 1

3. If there is a sequence of mutually exclusive events (B1, B2, B3, etc.) and C represents the event “at least one of the Bi’s occurs, then the probability of C is the sum of the probabilities of the Bis (p(C) = Σ p(Bi)

1. 0 < p(A) < 1 (in Venn diagrams)

AS

A

S

The probability of event A is between 0 and 1

2. p(S) = 1

AS

The probability of ANevent, in S, occurring is 1

3. p(C) = Σp(Bi)

B1

B3

B4 B2

B5

S

C

If the events B1, B2, B3, etc. are mutually exclusive, the probability of one of the Bs occurring is C, the sum of the Bs

Mutually Exclusive Events If A and B are mutually exclusive,

meaning that an event of type A precludes event B from occurring, by the 3rd axiom of probability

Mutually Exclusive Events If A and B are mutually exclusive, and

set A and set B are not null sets,

Joint Events If the events are independent, (not

mutually exclusive), meaning that the occurrence of one does not affect the occurrence of the other, the intersection

Joint Events - ExampleWhat is the probability of selecting a

black marble and white marble in two successive selections?

Since each selection is independent, then

p(Black, White) = .5 • .25 = .125

Generalization from Joint Events

If A, B, C, and D are independent events, then:

What is the probability of selecting a white marble, then red, then white, then black?

What if the events are not independent?Conditional probability - the occurrence

of one event is influenced by another event

“Conditional Probability” refers to the probability of one event under the condition that the other event is known to have occurred

p(A | B) - read “the probability of A given that B has occurred”

Probability Theory and Hypothesis TestingA man comes up to you on the street

and says that he has a “special” quarter that, when flipped, comes up heads more often than tails

He says you can buy it from him for $1You say that you want to test the coin

before you buy itHe says “OK”, but you can only flip it 5

times

Probability Theory ExampleHow many heads would convince you

that it was a “special” coin?3?, 4?, 5?How “sure” do you want to be that it is

a “special” coin? What is the chance that he is fooling you and selling a “regular” quarter?

2 HypothesesThe coin is not biased, it’s a normal

quarter that you can get at any bank– The likelihood of getting a heads on a

single flip is 1/2, or .5The coin is a special

– The probability of getting a heads on a single flip is greater than .5

Hypothesis TestingLet’s assume that it is a regular, old

quarterp = .5 (the probability of getting a heads on a SINGLE toss is .5)

We flip the coin and get 4 heads. What is the probability of this result, assuming the coin is fair?

Note that this is a problem involving conditional probability : p(4/5 heads|coin is fair)

How do I solve this problem?Any Ideas?You might think that, using the rule of

Joint events, that:

NO!

Why not?You have just calculated the probability

of getting exactly H, H, H, H on four flips of our coin.

What is the probability of getting H, T, H, T on 4 flips?

Exactly the same as H, H, H, H…any single combination of 4 H and T are equally likely in this scenario.

Here they are:

HHTT

HTHT

TTTH HTTH HHHT

TTHT TTHH HHTH

THTT THTH HTHH

TTTT HTTT THHT THHH HHHH

0 Heads 1 heads 2 heads 3 heads 4 heads

All Possibilities: 4 flips of a coin

f = 1 4 6 4 1

Relative Frequency DistHeads f p

0 1 .0625

1 4 .25

2 6 .375

3 4 .25

4 1 .0625

Total 16 1.00

YES!Relative frequency and Probability are

related by Bernoulli’s theorem If I did this test again, would I get the

same result? (probably not) If I did it over and over again, what

results would we expect given a non-biased coin?

How many combinations?

What if I figured out the total number of

possible outcomes of this experiment, and I figured out the total number of

outcomes that had 4/5 heads?

Prob of 4/5 = Freq of 4/5

Total N of outcomesHow many outcomes?

LotsH, H, T, T, TT, H, T, H, TH, T, H, T, HETC. ETC. ETC.

How many 4 out of 5?5 flips(exactly) 4 heads1 possibility – H, H, H, H, TAnother – H, H, H, T, HMore – H, H, T, H, HAnd – H, T, H, H, HLastly – T, H, H, H, H

HHTTT TTHHH

HTTHT THHTH

HTHTT THTHH

THHTT HTTHH

THTHT HTHTH

HTTTT TTHHT HHTTH THHHH

THTTT HTTTH THHHT HTHHH

TTHTT THTTH HTHHT HHTHH

TTTHT TTHTH HHTHT HHHTH

TTTTT TTTTH TTTHH HHHTT HHHHT HHHHH

5 Flips: All possibilities

0 heads 1 head 2 heads 3 heads 4 heads 5 headsp

= .03125 .15625 .3125 .3125 .15625 .03125

At least 4 heads out of 5Given a Fair Coin:Getting at least 4 heads out of 5 flips is

p(4) + p(5)

.15625+.03125 = .1875

There is a 18.75% chance that, upon flipping a FAIR coin 5 times, you will get at least 4 heads.

B1

B3

B4 B2

B5

C

You gonna buy that quarter?What if this guy let you flip this quarter

100 times?How many times do you want to flip it?

(the more the better, yes? In the long run???)