# Probability

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```					      Probability
&
Standard Error of
the Mean
Definition Review
   Population: all possible cases
   Parameters describe the population
   Sample: subset of cases drawn from
the population
   Statistics describe the sample

Statistics = Parameters
Why Sample????
   Can afford it
Why Sample????
 Can afford it
 Can do it in reasonable time
Why Sample????
 Can afford it
 Can do it in reasonable time

 Can estimate the amount of error
(uncertainty) in statistics, allowing us
to generalize (within limits) to our
population
Even with True Random Selection

   Some error (inaccuracy) associated
with the statistics (will not precisely
match the parameters)
   sampling error: everybody is different
 The whole measured only if ALL the parts
are measured.
With unbiased sampling
   Know that the amount of error is
reduced as the n is increased
   statistics more closely approximate the
parameters
   Amount of error associated with
statistics can be evaluated
   estimate by how much our statistics may
differ from the parameters
Sample size Rules of thumb
   Larger n the better
   law of diminishing returns
   ie 100 to 200 vs 1500 to 1600
   \$\$\$ and time constraints
   Less variability in population => better
estimate in statistics
   reduce factors affecting variability
   control and standardization
Human beings
are
terrible
randomizers
True Random sampling: rare

 What population is the investigator
interested in???
 Getting a true random sample of any
population is difficult if not impossible
   subject refusal to participate
Catch 22
 NEVER know our true
population parameters, so
we are ALWAYS at risk of
making an error in
generalization
Probability
Backbone of inferential stats

   Probability: the number of times some
event is likely to occur out of the total
possible events

# particular event
p=    # of possible events
Backbone of inferential stats

   The classic: flip a coin
   heads vs tails: each at 1/2 (50%)
   flip 8x: what possible events (outcomes)??
   flip it 8 million times: what probable
Wayne Gretzky
Wayne Gretzky & probability

What is the
probability that a
geeky looking kid
from Brantford,
would meet, much
less marry, a movie
star?
Wayne’s famous quote:
Wayne Gretzky redux.
Life with Probability

All life depends on probabilities
Voltaire (1756)
   life insurance rates
   obesity
   smoking
   car insurance rates
   age
   previous accidents
   driving demerits
   flood insurance
The Ever-Changing Nature of %s

Never go for a 50-50 ball unless you're 80-20
sure of winning it.
Ian Darke

The 50/50/90 Rule: whenever you have
a 50/50 chance of guessing at something,
there’s a 90% chance you will guess wrong.
Menard’s Philosophy
How to Count Cards
We are going to show you how to count cards. Card
counting is not illegal. If caught counting cards you will
not be arrested. You will not be taken into the back
room and beaten unconscious, then dragged to the
desert and buried with the rest of the casino cheaters.
You will not get your fingers cut off with a butcher knife
by Michael Corleone. However, if caught counting cards
you may be banned from playing at that casino. You
have to be smart about counting cards and don't be too
obvious. You do not want to be banned from the casino
that you are sleeping at. If you are going to try your luck
at counting cards we suggest you go down the street to
a different casino in case you get caught. Use this
own gamblingandgaming@hotmail.com
information at yourFrom risk.
One of the most popular card counting systems
currently in use is the point count system, also known
as Hi-Low. This system is based on assigning a point
value of +1, 0, or -1 to every card dealt to all players on
the table, including the dealer. Each card is assigned its
own specific point value. Aces and 10-point cards are
assigned a value of -1. Cards 7, 8, 9 each count as 0.
Cards 2, 3, 4, 5, and 6 each count as +1. As the cards
are dealt, the player mentally keeps a running count of
the cards exposed, and makes wagering decisions
based on the current count total.
•The higher the plus count, i.e. the higher percentage of
ten-point cards and aces remaining to be dealt, means
that the advantage is to player and he/she should
increase their wager.
•If the running count is around zero, the deck or shoe is
neutral and neither the player nor the dealer has an
• The higher the minus count, the greater disadvantage
it is to the player, as a higher than normal number of
'stiff' cards remains to be dealt. In this case a player
should be making their minimum wager or leave the
As the dealing of the cards progresses, the credibility
of the count becomes more accurate, and the size of
the player's wager can be increased or decreased with
a better probability of winning when the deck or shoe is
rich in face cards and aces, and betting and losing less
when the deck is rich in 'stiff' cards. It is important to
note that a player's decision process, when to hit,
stand, double down, etc. is still based on basic strategy.
Remember, you MUST learn basic strategy. However,
alterations in basic strategy play is sometimes
recommended based on the current card count.
For example, if the running count is +2 or greater and
you have a hard 16 against a dealer's up card of ten,
you should stand, which is a direct violation of basic
strategy. But considering that the deck or shoe is rich
in face cards you are more likely to bust in this
situation, thus you ignore basic strategy and stand.
Another example is to always take insurance when the
count is +3 or greater. For the most part however, you
should stick with basic strategy and use the card count
as an indication of when to increase or decrease the
amount of your bet, as that is the whole strategy behind
card counting.
Probability & the Normal
Curve
   Normal Curve
   mathematical abstraction
   unimodal
   symmetrical (Mean = Mode = Md)
   Asymptotic (any score possible)
   a family of curves
 Means the same, SDs are different
 Means are different, SDs the same

 both Means & SDs are different
Dice Roll Outcomes

Each dice has six equal possible outcomes when thrown -
numbers one through six.

The two dice thrown together have a total of 36 possible
outcomes, the six combinations of one dice by the six
combination of the other.
Dice Roll Outcomes
Numbers         Combinations           Dice   Combinations
2               one                    1 1
3               two                    1 2,   2   1
4               three                  1 3,   3   1,   2   2
5               four                   1 4,   4   1,   2   3,   3   2
6               five                   1 5,   5   1,   2   4,   4   2, 3 3
7               six                    1 6,   6   1,   2   5,   5   2, 3 4, 4 3
8               five                   2 6,   6   2,   3   5,   5   3, 4 4
9               four                   3 6,   6   3,   4   5,   5   4
10              three                  4 6,   6   4,   5   5
11              two                    5 6,   6   5
12              one                    6 6

Notice how certain totals have more possibilities
of being thrown, or are more probable of occurring
by random throw of the two dice.
Probability & the Normal
Curve
   99.7% of ALL cases within plus or minus 3
Standard Deviations
   Any score is possible
   but some more likely than others (which one?)
   Using the NC table
   Mean = 50
   SD = 7
   What is probability of getting a score > 64?
   one-tailed probability
Probability & the Normal
Curve
   Using the NC table
   What is probability of getting a score
that is more than one SD above OR
more than one SD below the mean?
 two-tailed   probability
Defining probable or likely
   What risk are YOU willing to take?
   Fly to Europe for \$1,000,000
   BUT…
   50% chance plane will crash
   25% chance
   1%chance
   .001% chance
   .000000001% chance
Defining probable or likely
   In science, we accept as unlikely to
have occurred at random (by chance)
 5% (0.05)              May be
 1% (0.01)              one-tailed
or two-tailed
 10% (0.10)
Serious people take
seriously probabilities,
not mere possibilities.
George Will, 11/2/2000
Six monkeys fail
to write
Shakespeare
Pantagraph, May 2003
Probability & the Normal Curve

   Any score is possible, but some more
likely than others
   Key to any problem in statistical inference
is to discover what sample values will
occur in repeated sampling and with what
probability.
With what probability will a score arise
by chance that is as extreme
as a certain value????
Statistics Humour
A man who travels a lot was concerned
about the possibility of a bomb on board
his plane. He determined the probability
of this, found it to be low but not low
enough for him. So now he always travels
with a bomb in his suitcase. He reasons
that the probability of two bombs being
on board would be infinitesimal.
Sampling
Distributions:

Standard error of the
mean
Recall
   With sampling, we EXPECT error in
our statistics
   statistics not equal to parameters
 cause:   random (chance) errors
Recall
   With sampling, we EXPECT error in
our statistics
   statistics not equal to parameters
 cause:   random (chance) errors
   Unbiased sampling: no factor(s)
systematically pushing estimate in a
particular direction
Recall
   With sampling, we EXPECT error in our
statistics
   statistics not equal to parameters
   cause: random (chance) errors
   Unbiased sampling: no factors
systematically pushing estimate in a
particular direction
   Larger sample = less error
Central Limit Theorem
   Consider (conceptualize) a distribution of
sample means drawn from a distribution
 repeated sampling (calculating mean) from
the same population
 produces a distribution of sample means
Central Limit Theorem
   A distribution of sample means drawn
from a distribution (the sampling
distribution of means) will be a normal
distribution
   class: from list of 51 state taxes, each
student create 5 random samples of n = 6.
   Look at distribution in SPSS
   Mp = 32.7 cents, SD = 18.1 cents
Central Limit Theorem
   Mean of distribution of sampling
means equals population mean if the
n of means is large


Central Limit Theorem
   Mean of distribution of sampling
means equals population mean if the
n of means is large
   true even when population is skewed if
sample is large (n > 60)
Central Limit Theorem
   Mean of distribution of sampling means
equals population mean if the n of
means is large
   true if population when skewed if sample is
large (n > 60)
   SD of the distribution of sampling
means is the Standard Error of the
Mean
Take home lesson
   We have quantified the expected error
(estimate of uncertainty) associated with
our sample mean
   Standard Error of the Mean
   SD of the distribution of sampling means
Typical procedure
   Sample
   calculate mean & SD
Typical procedure
   Sample
   calculate mean & SD
   KNOW & RECOGNIZE that
Typical procedure
   Sample
   calculate mean & SD
   KNOW & RECOGNIZE that
   statistics are not exact estimates of
parameters
Typical procedure
   Sample
   calculate mean & SD
   KNOW & RECOGNIZE that
   statistics are not exact estimates of
parameters
   a larger n provides a less variable measure of
the mean
Central Limit Theorem
Typical procedure
   Sample, calculate mean & SD
   KNOW & RECOGNIZE that
   statistics are not exact estimates of the
parameters
   a larger n provides a less variable measure of
the mean
   sampling from a population with low variability
gives a more precise estimate of the mean
Estimating Sample SEm
Example Calculation
•Mean = 75
•SDp = 16
•n = 64
•SEm = ???
Confidence Interval for the
Mean
•Mean = 75   Distribution of
•SDp = 16    sampling means

•n = 64
•SEm = 2

68%
Confidence Interval for the
Mean
•Mean = 75                       We are about
68% sure that
•SDp = 16                        population mean
Sample
•n = 64      mean                lies between 73
•SEm = 2                         and 77

73   75       77

68%
Confidence Interval for the
Mean
•Mean = 75
•SDp = 16
Sample
•n = 64                  mean
•SEm = 2

73 and 77 are the
upper and lower
limits of the 68%
confidence interval                73   75    77
for the population mean
68%
Example Calculation
•Mean = 75
•SDp = 16
•n = 16
•SEm = ???
Example Calculation
•Mean = 75
•SDp = 16
•n = 640
•SEm = ???
Example Calculation
•Mean = 75
•SDp = 160
•n = 16
•SEm = ???
Example Calculation
•Mean = 75
•SDp = 160
•n = 640
•SEm = ???
Explain how SD and n
affect the error inherent
in estimating the
population mean
95 % Confidence Interval for
the Mean
•Mean = 80             Distribution of
sampling means
•SDp = 20
•n = 36
•SEm = ??

??        80      ??
??                     ??
95 % Confidence Interval for
the Mean
•Mean = 80            Limits  X  1.96  SE M
•SDp = 20                      1.96 * 3.33 = 6.53
•n = 36                        Up = 80 + 6.53
•SEm = 3.33                    Lo = 80 - 6.53

73.34   76.67   80       83.33        86.66

95%
95 % Confidence Interval for
the Mean
•Mean = 80
•SDp = 20
Sample
•n = 36               mean
•SEm = 3.33
73.47                         86.53
73.47 and 86.53 are the
upper and lower
limits of the 95%
confidence interval
73.34     76.67   80    83.33     86.66
for the population mean
95%
Key to any problem in statistical
inference is to discover what
sample values will occur in
repeated sampling and
with what probability.

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 views: 99 posted: 2/24/2010 language: English pages: 66