#### Transcript Hypothesis Testing

```Class 9
March 23, 2006
• From exam, just review one part of calculations
from normal distribution
• Total carb intake: µ = 124g/1000cal,
δ = 20g/1000cal.
• For every 100 boys, how many would have carb
intake between 90 & 110g/1000cal?
• 2 points to note here: 1. asking for number, not
percentage. 2. draw the normal distribution.
Review estimate of the mean
Confidence intervals,
that is,
what is the probability that a population
mean will fall within a certain range based
on an estimate using a sample mean.
Usual Intervals
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•
•
•
•
Usually use 90%, 95% or 99%
What are the corresponding Z values?
Last time we found that Z = 1.96
Just accept that for the moment; we will
review how to get it shortly.
Example
Estimate the mean birth weight of newborns with
gestational age 40 weeks, based on a sample of
5 such newborns with a mean birth weight of
3500 grams and a standard deviation of 430
grams.
Since there are only 5 in the sample, what do we
have to assume?
Have to assume that the population from which
the sample is drawn is approximately normal
and that these are 5 randomly chosen
newborns.
Mu is between the two values calculated by:
X  Z * σ/ n
X  Z * σ/ n
Always draw the N.D.
The shaded area can help us to see what
we mean by X  Z * σ/ n
It is the border of the shaded area to the right of the mean .
We are saying that the mean lies between that border and the
corresponding left-side border
Write the equation


X  Z * σ/ n 
μ  X  Z * σ/
n
Mu lies between the two values within the
parentheses

Practical Statement of Result
• With 95% confidence, we can say that u
will be ≤ 1.96 *S.E. and
• ≥ -1.96 * S.E.
• We call 1.96 the reliability coefficient
The Math
• X = 3500 g,
n = 5,
б = 430
• Review what the symbols mean
• Find the quantity Z * б / Γn
б / Γn = 430/ Γ5 = 430/ 2.24 = 192
• 1.96 * 192 = 376
• Mu is between 3500 – 376 and
3500 + 376
• 3124 ≤ µ ≤ 3876 with 95% confidence
How did we get Z
• Review for 95%
what is the area not
included
divide into 2 tails
look up Z
• Try for 90%
• Try for 99%
Applications
• If we know a population mean & st. dev.,
we can calculate the probability that any
sample will have a stated mean.
• A certain large human pop’n has a cranial
length that is approx’ly normally distributed
with mean 185.6 mm and б of 12.7 mm.
µ = 185.6 mm
б = 12.7 mm
• What is the probability that a random
sample of size 10 from this population will
have a mean greater than 190?
• We can calculate this probability but why
would we?
Usefulness??
• Let’s say that it is accepted knowledge
that the population has a certain mean.
• I am working with a group of people.
• I want to know if they fit into this
population with regard to the particular
parameter. If the probability of the mean
of the sample is very low, perhaps it is not
really from the same population
Education Example
• Third-graders in the U.S. have an average
• Third-graders in a particular school have a
mean reading score of 120. What’s the
probability that they are from the same
population?
Back to Cranial Length
• µ = 185.6 mm б = 12.7 mm
• random sample of size 10 from this
population will have a mean greater
than 190?
• Have to find how far 190 is from 185.6
in units of standard error of the mean
Z  (190 185.6)
12.7/ 10
Probability of Mean of 190
Did you draw a normal
dist???
Z = 4.4
/ 12.7 / 3.16
Z = 1.09
0.138
Area = 0.138
185.6 190
The probability is 13.8%
0
1.09
Hypothesis Testing
A frequently used method in
clinical research
Another Look at Confidence
Intervals
• 95% C.I.: The space in each tail is 0.025.
• The sum is 0.050
• This value, the space not covered in the
C.I., is called alpha.
• α is called the Significance Level
• Each tail contains α/2
Using a Confidence Interval
• A school district administrator took sample(s) of
Nassau County and then estimated a 90% C.I.
for the mean: 87.60 to 92.34 with a sigma of
10.
• All the individual schools then compared their
mean to this.
• If they did not fall within the interval, they were
considered significantly better or significantly
worse and faculty hiring's were affected by the
results.
Hypothesis Testing
Just a new way to ask the
questions. The methods used are
virtually the same as C.I.’s
The More Usual Approach
• The most frequent application of
estimating the mean is to look for effects
that put a group outside the interval
.
• We usually set up research so that we are
looking for a difference
A New Drug to Lower Cholesterol
• Everyone has been using my competitors
product named reductum
• I have developed a drug called
drasticchangum
• I want to find out if my product has results
different from the accepted treatment
Set Up a Hypothesis
• My hope is that there is a difference
between my product and theirs.
• I set up Hypothesis A that says there is a
difference
• In order to test it, I state another
hypothesis saying that there is NO
DIFFERENCE between the 2 treatments.
• I want to reject this hypothesis
Another Wording
• HA says that the sample comes from a
population with a mean different from that
of the original population
• H0 says that the sample comes from a
population with a mean the same as that
of the original population. We call this µ0
The Hypothesis to Reject
• This is called the Null Hypothesis, H0
• I now go about the process to reject it.
• I have to choose a significance level
• Let’s say α = 0.01, analogous to a C.I. of 99%
Set-Up
• Each tail has probability of α/2. Call it
Rejection Zone
• For α = 0.01, each tail has 0.005 and this
occurs for any Z that is over 2.54
Check the table of Normal Distribution
Results
• The population treated by reductum has a mean
change in cholesterol of 30 units with a sigma of
4.5.
• I have a preliminary sample of 10 patients
treated by drasticchangum. Their mean change
is 34 units. I assume that they come from a
population that has the same sigma as the first.
Hypotheses
• HA: Called the Alternative Hypothesis.
This is the result I want to support:
“µ for the new population differs from 30.
µ ≠ 30.”
• H0: The Null Hypothesis, I want to reject
this:
µ for the new population is the same as
the old, that is 30.
“µ = 30”.
The Test Statistic
• Z = (X - µ0) / б/Γn
• Z = (34 – 30) / 4.5 / Γ10
• Z = 2.81
• This is larger than 2.54 so is in the rejection
zone
Excellent Result
• We can reject the null hypothesis
• We can accept the alternative hypothesis
• I can say that drasticchangum causes a
different change in cholesterol level from
reductum.
• Is there anything else we might prefer to
say?
• How about “Drasticchangum causes a
greater change than does reductum.”?
Different Hypotheses
• HA: The mean for DC is greater than 30.
• H0 : The mean for DC is equal to 30
• How can we reject H0?
One-Sided Tests
• So far we have divided alpha into the two
tails.
• Let’s put the whole alpha into just one tail.
• Can we still reject the null hypothesis at
the level of α = 0.01 (99% confidence)?
• Look up the tail with area = 0.01
• Now Z just has to exceed 2.30.
• It’s even easier to reject
Easier to Reject
• If we choose the correct side to consider
then it is easier to reject the null
hypothesis
• And accept the alternative hypothesis
• Remember to express the correct
alternative hypothesis before starting the
math.
The Two Things We Can Say
• We reject the null hypothesis that the
sample came from the original population
• We accept the alternative hypothesis that
the sample came from a population with a
mean greater than that of the original
population.
So…Nothing so new
• Hypothesis Testing is a cinch – because
you already knew most of it from
Confidence Intervals of the Normal
Distribution
• Let’s just look at some details
Confidence
• Not only are we saying that
drasticchangum has a greater effect than
does reductum
• But we have a level of significance, i.e. a
statement of probability that we are
correct.
• But that means our conclusion could also
be incorrect in reality even while satisfying
our statistical conditions.
Possible Errors
• In our example, we stated that the new
sample came from a different population
than the original
• We could be incorrect.
• What are the chances that we have stated
a difference when there isn’t one?
• The probability that the sample came from
the old population is 0.01
Rejection Zone
• If the new sample had a population mean
equal to the original mean, there is only a
0.01 possibility that the sample mean
would fall far enough from µ0 to give a Z of
2.30.
• This would be a rare event but a possible
one.
Type I Error
• If we say that there is a difference when there really isn’t
one. That is called a Type I Error.
• We were statistically allowed to reject the null hypothesis
but only some spiritual being somewhere knew that we
shouldn’t have done so.
• We may never know that the result was incorrect or …
• After 1000 patients have tried drasticchangum, we may
find that it came from a population with a mean the same
as that of reductum.
The chance of committing a
Type I error is equal to alpha.
OR…
• We may be able to state the probability
even more precisely.
• The value of “p”.
• Use the value calculated for Z, 2.81
• The area under the tail of the Normal
Distribution is 0.002
• The chance that the means are equal is
equal to or less than 0.002
• A report would state p ≤ 0.002
Conclusion
• The null hypothesis is rejected with a p
value less than or equal to 0.002
• The alternative hypothesis is accepted
stating that the effect of drasticchangum is
greater than that of reductum.
An Example
• Weight Watchers Inc claims that those
who have followed their program for at
least a yr maintain some weight loss even
after they have left the formal program.
• A competitive group claims that those who
have left the program have a rebound
effect and actually weigh more than they
would have if they had never tried the ww
program
Before Gathering Data
• HA: there is a difference between the
average for former weight watchers and
the general population
• H0: The mean weight for both groups is the
same. µest = µ0
• Test the null hypothesis with a significance
level of 0.05
• Is the test one-sided or two-sided?
Are the Wts different from the
general population
• Considering the population of women from
ages 30 to 60, it is accepted that the
average weight is 150 with a variance of
20 lbs.
• A sample of 20 former weight watchers
have an average weight of 147 pounds
Establish Zcrit & determine Zcalc
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For α = 0.05, & a 2-sided test, what is Zcrit?
Check the table for area of 0.025. Z = 1.96
Zcalc = (147-150)/Γ20/ Γ20 = 3/ Γ1 = 3
Do we reject the null hypothesis?
Do we accept the alternative hypothesis?
Yes to both and what is p?
p ≤ 0.001
Review p
• We got p by looking up the area
corresponding to a Zcalc of 3.0. Check it.
• What does it mean that p ≤ 0.001?
• It means that the chance of a Type I error
is ≤ 0.001.
• It means that the chance that the sample
came from the population with mean of
150 is ≤ 0.001
Could we have set up a different
test?
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Yes, we could have asked a one-sided type question.
How would WW Inc have asked the question?
One-sided: WW average is less than population
Is there an advantage for them in choosing this?
Yes, a one-sided test would have given a greater area in
the rejection zone.
Easier for them to show that their former clients weigh
less than the overall population
Would p be any different?
No. What is different?
Zcrit
Another Sample
• A pharmaceutical company says that it is
more effective to take a weight loss drug.
• They say they have a sample of 100
former weight watchers with an average
weight of 149.5.
• Now do a two-sided test on this.
Zcalc = (149.5-150)/Γ20/ Γ100 = 0.5/ Γ0.5 =
0.7
• Can we reject the null hypothesis?
• No
• Can we accept the null hypothesis that the
average weight of the former ww’s equals
the general population?
• NO, NO, NO, NO, NO, NO
• We never accept the null hypothesis
• All we can say is – not enough evidence to
reject.
• 149.5 is very close to 150
• Does that mean the sample came from the
population with the original mean
• No
• Could have come from a population with a
mean of 149.5, or 150.5 or 149.9 or 148 or
whaterver.
• We cannot accept the null hypothesis
So at least we don’t reject
• Is the pharmaceutical company a little
happier?
• Maybe but the ww’s aren’t.
• Could the ww’s be correct and the new
sample just didn’t show it?
• Yes, this is possible
• Called a Type II error
Type II Error
• We fail to reject a null hypothesis on
statistical grounds
• But that greater being in heaven knows
that we are incorrect. There is really a
difference even if small.
• And what can the ww’s do?
• They might take an even larger sample
and try to find a difference.
The two types of errors
• Type I: we rejected when the reality is that
there is no difference
• We can evaluate the chance of this error.
It is equal to alpha.
• Type II: we did not reject when the reality
is that there is a difference
• We cannot easily evaluate the chance of a
type II error.
The Power of a Test
• Whenever we do not reject the null
hypothesis, there is a chance of a Type II
Error. The chance of that error is called β
• In this course we will not learn how to
evaluate the chance of the error.
• The probability of NOT making a Type II
Error is called the Power of a Test.
• The Power of a Test can be increased
Two Ways to Decrease β, i.e.
Increase the Power
• One way is to make the rejection zone
larger
• But then we are increasing the chance of a
Type I Error
• The other way is to increase the sample
size
• We can calculate a value of n for predetermined values of α and β.
• We won’t cover this.
Practice Problem
• Chapter 10 # 12
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