Case Study: Hereditary Angioedema All responses must be in your own words. Answers that have been copied and pasted will not receive credit. 1. Translate “angioedema”. [Note: I am not looking for a description of the disorder. Rather, I would like you to translate the medical term itself.] 2. The complement system is described as a ‘cascade system’. How does the system fit into this description of being a cascade? [Suggestion: Google the definition of cascade, then think about the complement system in light of the definition] 3. Is complement involved in the innate, or the adaptive immune system, or both? Please explain you answer. 4. What role does C1INH play in the complement system? Why is it so important? 5. What was the physiologic cause of Richard’s abdominal pain? 6. How can one distinguish the swelling of HAE from the swelling of allergic angioedema? 7. What is bradykinin’s role in HA? 8. Do you think Richard’s infancy colic was related to his HA? No need to research this. Just use your intuition. Explain your thinking. 9. What is typically used to treat attacks of HAE? 10. Swelling in the extremities is not dangerous. What other areas of the body are subject to swelling? What is the most dangerous location for swelling to occur and why is it the most dangerous? 2018 BUS 308 Week 2 Lecture 1 Examining Differences - overview Expected Outcomes After reading this lecture, the student should be familiar with: 1. The importance of random sampling. 2. The meaning of statistical significance. 3. The basic approach to determining statistical significance. 4. The meaning of the null and alternate hypothesis statements. 5. The hypothesis testing process. 6. The purpose of the F-test and the T-test. Overview Last week we collected clues and evidence to help us answer our case question about males and females getting equal pay for equal work. As we looked at the clues presented by the salary and comp-ratio measures of pay, things got a bit confusing with results that did not see to be consistent. We found, among other things, that the male and female compa-ratios were fairly close together with the female mean being slightly larger. The salary analysis showed a different view; here we noticed that the averages were apparently quite different with the males, on average, earning more. Contradictory findings such as this are not all that uncommon when examining data in the “real world.” One issue that we could not fully address last week was how meaningful were the differences? That is, would a different sample have results that might be completely different, or can we be fairly sure that the observed differences are real and show up in the population as well? This issue, often referred to as sampling error, deals with the fact that random samples taken from a population will generally be a bit different than the actual population parameters, but will be “close” enough to the actual.

1. Case Study: Hereditary Angioedema
All responses must be in your own words. Answers that have
been copied and pasted will not receive credit.
1. Translate “angioedema”. [Note: I am not looking for a
description of the disorder. Rather, I would like you to
translate the medical term itself.]
2. The complement system is described as a ‘cascade system’.
How does the system fit into this description of being a
cascade? [Suggestion: Google the definition of cascade, then
think about the complement system in light of the definition]
3. Is complement involved in the innate, or the adaptive immune
system, or both? Please explain you answer.
4. What role does C1INH play in the complement system?
Why is it so important?
5. What was the physiologic cause of Richard’s abdominal pain?
6. How can one distinguish the swelling of HAE from the
swelling of allergic angioedema?
7. What is bradykinin’s role in HA?
8. Do you think Richard’s infancy colic was related to his HA?
No need to research this. Just use your intuition. Explain your
thinking.
9. What is typically used to treat attacks of HAE?
10. Swelling in the extremities is not dangerous. What other
areas of the body are subject to swelling? What is the most

2. dangerous location for swelling to occur and why is it the most
dangerous?
2018
BUS 308 Week 2 Lecture 1
Examining Differences - overview
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. The importance of random sampling.
2. The meaning of statistical significance.
3. The basic approach to determining statistical significance.
4. The meaning of the null and alternate hypothesis statements.
5. The hypothesis testing process.
6. The purpose of the F-test and the T-test.
Overview
Last week we collected clues and evidence to help us answer
our case question about
males and females getting equal pay for equal work. As we
looked at the clues presented by the
salary and comp-ratio measures of pay, things got a bit
confusing with results that did not see to
be consistent. We found, among other things, that the male and
female compa-ratios were fairly
close together with the female mean being slightly larger. The
salary analysis showed a different
view; here we noticed that the averages were apparently quite
different with the males, on

3. average, earning more. Contradictory findings such as this are
not all that uncommon when
examining data in the “real world.”
One issue that we could not fully address last week was how
meaningful were the
differences? That is, would a different sample have results that
might be completely different, or
can we be fairly sure that the observed differences are real and
show up in the population as
well? This issue, often referred to as sampling error, deals with
the fact that random samples
taken from a population will generally be a bit different than the
actual population parameters,
but will be “close” enough to the actual values to be valuable in
decision making.
This week, our journey takes us to ways to explore differences,
and how significant these
differences are. Just as clues in mysteries are not all equally
useful, not all differences are
equally important; and one of the best things statistics will do
for us is tell us what differences
we should pay attention to and what we can safely ignore.
Side note; this is a skill that many managers could benefit from.
Not all differences in
performances from one period to another are caused by
intentional employee actions, some are
due to random variations that employees have no control over.
Knowing which differences to
react to would make managers much more effective.
In keeping with our detective theme, this week could be
considered the introduction of
the crime scene experts who help detectives interpret what the

4. physical evidence means and how
it can relate to the crime being looked at. We are getting into
the support being offered by
experts who interpret details. We need to know how to use
these experts to our fullest
advantage. ��
Differences
In general, differences exist in virtually everything we measure
that is man-made or
influenced. The underlying issue in statistical analysis is that at
times differences are important.
When measuring related or similar things, we have two types of
differences: differences in
consistency and differences in average values. Some examples
of things that should be the
“same” could be:
• The time it takes to drive to work in the morning.
• The quality of parts produced on the same manufacturing line.
• The time it takes to write a 3-page paper in a class.
• The weight of a 10-pound bag of potatoes.
• Etc.
All of these “should” be the same, as each relates to the same
outcome. Yet, they all differ. We
all experience differences in travel time, and the time it takes to
produce the same output on the
job or in school (such as a 3-page paper). Production standards
all recognize that outcomes
should be measured within a range rather than a single point.
For example, few of us would be
upset if a 10-pound bag of potatoes weighed 9.85 pounds or

5. would think we were getting a great
deal if the bag weighed 10.2 pounds. We realize that it is
virtually impossible for a given
number of potatoes to weigh exactly the same and we accept
this as normal.
One reason for our acceptance is that we know that variation
occurs. Variation is simply
the differences that occur in things that should be “the same.”
If we can measure things with
enough detail, everything we do in life has variation over time.
When we get up in the morning,
how long it takes to get to work, how effective we are at doing
the same thing over and over, etc.
Except for physical constants, we can say that things differ and
we need to recognize this. A side
note: variation exists in virtually everything we study (we have
more than one language, word,
sentence, paragraph, past actions, financial transactions, etc.),
but only in statistics do we bring
this idea front and center for examination.
This suggests that any population that we are interested in will
consist of things that are
slightly different, even if the population contains only one
“thing.” Males are not all the same,
neither are females. Manufactured parts differ in key
measurements; this is the reason we have
quality control checking to make sure the differences are not
too large. So, even if we measure
everything in our population we will have a mean that is
accompanied by a standard deviation
(or range). Managers and professionals need to manage this
variation, whether it is quantitative
(such as salary paid for similar work) or even qualitative (such
as interpersonal interactions with

6. customers).
The second reason that we are so concerned with differences is
that we rarely have all the
evidence, or all the possible measures of what we are looking
for. Having this would mean we
have access to the entire population (everything we are
interested in); rarely is this the case.
Generally, all decisions, analysis, research, etc. is done with
samples, a selected subset of the
population. And, with any sample we are not going have all the
information needed, obviously;
but we also know that each sample we take is going to differ a
bit. (Remember, variation is
everywhere, including in the consistency of sample values.) If
you are not sure of this, try
flipping a coin 10 times for 10 trials, do you expect or get the
exact same number of heads for
each trial? Variation!
Since we are making decisions using samples, we have even
more variation to consider
than simply that with the population we are looking at. Each
sample will be slightly different
from its population and from others taken from the same
population.
How do we make informed decisions with all this variation and
our not being able to
know the “real” values of the measures we are using? This
question is much like how detectives
develop the “motive” for a crime – do they know exactly how
the guilty party felt/thought when

7. they say “he was jealous of the success the victim had.” This
could be true, but it is only an
approximation of the true feelings, but it is “close enough” to
say it was the reason. It is similar
with data samples, good ones are “close enough” to use the
results to make decisions with. The
question we have now focuses on how do we know what the
data results show?
The answer lies with statistical tests. They can use the
observed variation to provide
results that let us make decisions with a known chance of being
wrong! Most managers hope to
be right just over 50% of the time, a statistical decision can be
correct 95% or more of the time!
Quite an improvement.
Sampling. The use of samples brings us to a distinction in
summary statistics, between
descriptive and inferential statistics. With one minor exception
(discussed shortly), these two
appear to be the same: means, standard deviations, etc.
However, one very important distinction
exists in how we use these. Descriptive statistics, as we saw
last week, describes a data set. But,
that is all they do. We cannot use them to make claims or
inferences about any other larger
group.
Making inferences or judgements about a larger population is
the role of inferential
statistics and statistical tests. So, what makes descriptive
statistics sound enough to become
inferential statistics? The group they were taken from! If we
have a sample that is randomly
selected from the population (meaning that each member has the

8. same chance of being selected
at the start), then we have our best chance of having a sample
that accurately reflects the
population, and we can use the statistics developed from that
sample to make inferences back to
the population. (How we develop a randomly selected sample is
more of a research course issue,
and we will not go into these details. You are welcome to
search the web for approaches.)
Random Sampling. If we are not working with a random
sample, then our descriptive
statistics apply only to the group they are developed for. For
example, asking all of our friends
their opinion of Facebook only tells us what our friends feel; we
cannot say that their opinions
reflect all Facebook users, all Facebook users that fall in the
age range of our friends, or any
other group. Our friends are not a randomly selected group of
Facebook users, so they may not
be typical; and, if not typical users, cannot be considered to
reflect the typical users.
If our sample is random, then we know (or strongly suspect) a
few things. First, the
sample is unlikely to contain both the smallest and largest value
that exists in the larger
population, so an estimate of the population variation is likely
to be too small if based on the
sample. This is corrected by using a sample standard deviation
formula rather than a population
formula. We will look at what this means specifically in the
other lectures this week; but Excel

9. will do this for us easily.
Second, we know that our summary statistics are not the same
as the population’s
parameter values. We are dealing with some (generally small)
errors. This is where the new
statistics student often begins to be uncomfortable. How can we
make good judgements if our
information is wrong? This is a reasonable question, and one
that we, as data detectives, need to
be comfortable with.
The first part of the answer falls with the design of the sample,
by selecting the right
sample size (how many are in the sample), we can control the
relative size of the likely error.
For example, we can design a sample where the estimated error
for our average salary is about
plus or minus $1,000. Does knowing that our estimates could
be $1000 off change our view of
the data? If the female average was a thousand dollars more
and the male salary was a thousand
dollars less, would you really change your opinion about them
being different? Probably not
with the difference we see in our salary values (around 38K
versus 52K). If the actual averages
were closer together, this error range might impact our
conclusions, so we could select a sample
with a smaller error range. (Again, the technical details on how
to do this are found in research
courses. For our statistics class, we assume we have the correct
sample.)
Note, this error range is often called the margin of error. We
see this most often in
opinion polls. For example, if a poll said that the percent of

10. Americans who favored Federal
Government support for victims of natural disasters (hurricanes,
floods, etc.) was 65% with a
margin of error of +/- 3%; we would say that the true proportion
was somewhat between 62% to
68%, clearly a majority of the population. Where the margin of
error becomes important to
know is when results are closer together, such as when support
is 52% in favor versus 48%
opposed, with a margin of error of 3%. This means the actual
support could be as low as 49% or
as high as 55%; meaning the results are generally too close to
make a solid decision that the issue
is supported by a majority, the proverbial “too close to call.”
The second part of answering the question of how do we make
good decisions introduces
the tools we will be looking at this week, decision making
statistical tests that focus on
examining the size of observed differences to see if they are
“meaningful” or not. The neat part
of these tools is we do not need to know what the sampling
error was, as the techniques will
automatically include this impact into our results!
The statistical tools we will be looking at for the next couple of
weeks all “work” due to a
couple of assumptions about the population. First, the data
needs to be at the interval or ratio
level; the differences between sequential values needs to be
constant (such as in temperature or
money). Additionally, the data is assumed to come from a
population that is normally
distributed, the normal curve shape that we briefly looked at
last week. Note that many
statisticians feel that minor deviations from these strict

11. assumptions will not significantly impact
the outcomes of the tests.
The tools for this week and next use the same basic logic. If we
take a lot of samples
from the population and graph the mean for all of them, we will
get a normal curve (even if the
population is not exactly normal) distribution called the
sampling distribution of the mean.
Makes sense as we are using sample means. This distribution
has an overall, or grand, mean
equal to that of the population. The standard deviation equals
the standard deviation of the
population divided by the square root of the population. (Let’s
take this on faith for now, trust
me you do not want to see the math behind proving these. But
if you do, I invite you to look it
up on the web.) Now, knowing – in theory – what the mean
values will be from population
samples, we can look at how any given sample differs from
what we think the population mean
is. This difference can be translated into what is essentially a
z-score (although the specific
measure will vary depending upon the test we are using) that we
looked at last week. With this
statistic, we can determine how likely (the probability of)
getting a difference as large or larger
than we have purely by chance (sampling error from the actual
population value) alone.
If we have a small likelihood of getting this large of a
difference, we say that our
difference is too large to have been purely a sampling error, and
we say a real difference exists or

12. that the mean of the population that the sample came from is not
what we thought.
That is the basic logic of statistical testing. Of course, the
actual process is a bit more
structured, but the logic holds: if the probability of getting our
result is small (for example 4% or
0.04), we say the difference is significant. If the probability is
large (for example 37% or 0.37),
then we say there is not enough evidence to say the difference is
anything but a simple sampling
error difference from the actual population result.
The tools we will be adding to our bag of tricks this week will
allow us to examine
differences between data sets. One set of tools, called the t-
test, looks at means to see if the
observed difference is significant or merely a chance difference
due mostly to sampling error
rather than a true difference in the population. Knowing if
means differ is a critical issue in
examining groups and making decisions.
The other tool – the F-test for variance, does the same for the
data variation between
groups. Often ignored, the consistency within groups is an
important characteristic in
understanding whether groups having similar means can be said
to be similar or not. For
example, if a group of English majors all took two classes
together, one math and one English,
would you expect the grade distributions to be similar, or would
you expect one to show a larger
range (or variation) than the other?
We will see throughout the class that consistency and

13. differences are key elements to
understanding what the data is hiding from us, or trying to tell
us – depending on how you look
at it. In either case, as detectives our job is to ferret out the
information we need to answer our
questions.
Hypothesis Testing-Are Differences Meaningful
Here is where the crime scene experts come in. Detectives have
found something but are
not completely sure of how to interpret it. Now the training and
tools used by detectives and
analysts take over to examine what is found and make some
interpretations. The process or
standard approach that we will use is called the hypothesis
testing procedure. It consists of six
steps; the first four (4) set up the problem and how we will
make our decisions (and are done
before we do anything with the actual data), the fifth step
involves the analysis (done with
Excel), and the final and sixth step focuses on interpreting the
result.
The hypothesis testing procedure is a standardized decision-
making process that ensures
we make our decisions (on whether things are significantly
different or not) is based on the data,
and not some other factors. Many times, our results are more
conservative than individual
managerial judgements; that is, a statistical decision will call
fewer things significantly different
than many managerial judgement calls. This statistical

14. tendency is, at times, frustrating for
managers who want to show that things have changed. At other
times, it is a benefit such as if
we are hoping that things, such as error rates, have not changed.
While a lot of statistical texts have slightly different versions of
the hypothesis testing
procedure (fewer or more steps), they are essentially the same,
and are a spinoff of the scientific
method. For this class, we will use the following six steps:
1. State the null and alternate hypothesis
2. Select a level of significance
3. Identify the statistical test to use
4. State the decision rule. Steps 1 – 4 are done before we
examine the data
5. Perform the analysis
6. Interpret the result.
Step 1
A hypothesis is a claim about an outcome. It comes in two
forms. The first is the null
hypothesis – sometimes called the testable hypothesis, as it is
the claim we perform all of our
statistical tests on. It is termed the “Null” hypothesis, shown as
Ho, as it basically says “no
difference exists.” Even if we want to test for a difference,
such as males and females having a
different average compa-ratio; in statistics, we test to see if
they do not.
Why? It is easier to show that something differs from a fixed
point than it is to show that
the difference is meaningful – I mean how can we focus on
“different?” What does “different”

15. mean? So, we go with testing no difference. The key rule
about developing a null hypothesis is
that it always contains an equal claim, this could be equal (=),
equal to or less than (<=), or equal
to or more than (=>).
Here are some examples:
Ex 1: Question: Is the female compa-ratio mean = 1.0?
Ho: Female compa-ratio mean = 1.0.
Ex 2: Q: is the female compa-ratio mean = the male compa-
ratio mean?
Ho: Female compa-ratio mean = Male compa-ratio mean.
Ex. 3: Q: Is the female compa-ratio more than the male compa-
ratio? Note that this
question does not contain an equal condition. In this case, the
null is the opposite of what
the question asks:
Ho: Female compa-ratio <= Male compa-ratio.
We can see by testing this null, we can answer our initial
question of a directional
difference. This logic is key to developing the correct test
claim.
A null hypothesis is always coupled with an alternate
hypothesis. The alternate is the
opposite claim as the null. The alternate hypothesis is shown as
Ha. Between the two claims, all

16. possible outcomes must be covered. So, for our three examples,
the complete step 1 (state the
null and alternate hypothesis statements) would look like:
Ex 1: Question: Is the female compa-ratio mean = 1.0?
Ho: Female compa-ratio mean = 1.0.
Ha: Female compa-ratio mean =/= (not equal to) 1.0
Ex 2: Q: is the female compa-ratio mean = the male compa-
ratio mean?
Ho: Female compa-ratio mean = Male compa-ratio mean.
Ha: Female compa-ratio mean =/= Male compa-ration mean.
Ex. 3: Q: Is the female compa-ratio more than the male compa-
ratio?
Ho: Female compa-ratio <= Male compa-ratio
Ha: Female compa-ratio > Male compa-ratio. (Note that in this
case, the alternate
hypothesis is the question being asked, but the null is what we
always use as the
test hypothesis.)
When developing the null and alternate hypothesis,
1. Look at the question being asked.
2. If the wording implies an equality could exist (equal to, at
least, no more than, etc.),
we have a null hypothesis and we write it exactly as the
question asks.

17. 3. If the wording does not suggest an equality (less than, more
than, etc.), it refers to the
alternate hypothesis. Write the alternate first.
4. Then, for whichever hypothesis statement you wrote, develop
the other to contain all
of the other cases. An = null should have a =/= alternate, an =>
null should have a <
alternate; a <= null should have a > alternate, and vice versa.
5. The order the variables are listed in each hypothesis must be
the same, if we list
males first in the null, we need to list males first in the
alternate. This minimizes
confusion in interpreting results.
Note: the hypothesis statements are claims about the population
parameters/values based
on the sample results. So, when we develop our hypothesis
statements, we do not consider the
sample values when developing the hypothesis statements. For
example, consider our desire to
determine if the compa-ratio and salary means for males and
females are different in the
population, based on our sample results. While the compa-ratio
means seemed fairly close
together, the salary means seemed to differ by quite a bit; in
both cases, we would test if the male
and female means were equal since that is the question we have
about the values in the
population.

18. If you look at the examples, you can notice two distinct kinds of
null hypothesis
statements. One has only an equal sign in it, while the other
contains an equal sign and an
inequality sign (<=, but it could be =>). These two types
correspond to two different research
questions and test results.
If we are only interested in whether something is equal or not,
such as if the male average
salary equals the female average salary; we do not really care
which is greater, just if they could
be the same in the population or not. For our equal salary
question, it is not important if we find
that the male’s mean is > (greater than) the female’s mean or if
the male’s mean is < (less than)
the female’s mean; we only care about a difference existing or
not in the population. This, by the
way, is considered a two-tail test (more on this later), as either
conditions would cause us to say
the null’s claim of equality is wrong: a result of “rejecting the
null hypothesis.”
The other condition we might be interested in, and we need a
reason to select this
approach, occurs when we want to specifically know if one
mean exceeds the other. In this
situation, we care about the direction of the difference. For
example, only if the male mean is
greater than the female mean or if the male mean is less than the
female mean.
Step 2
The level of significance is another concept that is critical in
statistics but is often not

19. used in typical business decisions. One senior manager told the
author that their role was to
ensure that the “boss’ decisions were right 50% +1 of the time
rather than 50% -1.” This
suggests that the level of confidence that the right decisions are
being made is around 50%. In
statistics, this would be completely unacceptable.
A typically statistical test has a level of confidence that the
right decision is being made is
about 95%, with a typical range from 90 to 99%. This is done
with our chosen level of
significance. For this class, we will always use the most
common level of 5%, or more
technically alpha = 0.05. This means we will live with a 5%
chance of saying a difference is
significant when it is not and we really have only a chance
sampling error.
Remember, no decision that does not involve all the possible
information that can be
collected will ever have a zero possibility of being wrong. So,
saying we are 95% sure we made
the right call is great. Marketing studies often will use an alpha
of .10, meaning that are 90%
sure when they say the marketing campaign worked. Medical
studies will often use an alpha of
0.01 or even 0.001, meaning they are 99% or even 99.9% sure
that the difference is real and not
a chance sampling error.
Step 3
Choosing the statistical test and test statistic depends upon the

20. data we have and the
question we are asking. For this week, we will be using compa-
ratio data in the examples and
salary data in the homework – both are continuous and at least
interval level data. The questions
we will look at this week will focus on seeing if there is a
difference in the average pay (as
measured by either the compa-ratio or salary) between males
and females in the population,
based on our sample results. After all, if we cannot find a
difference in our sample, should we
even be working on the question?
In the quality improvement world, one of the strategies for
looking for and improving
performance of a process is to first look at and reduce the
variation in the data. If the data has a
lot of variation, we cannot really trust the mean to be very
reflective of the entire data set.
Our first statistical test is called the F-test. It is used when we
have at least interval level
data and we are interested in determining if the variances of two
groups are significantly
different or if the observed difference is merely chance
sampling error. The test statistic for this
is the F.
Once we know if the variances are the same or not, we can
move to looking for
differences between the group means. This is done with the T-
test and the t-statistic. Details on
these two tests will be given later; for now, we just need to
know what we are looking at and
what we will be using.

21. Step 4
One of the rules in researching questions is that the decision
rule, how we are going to
make our decision once the analysis is done, should be stated
upfront and, technically, even
before we even get to the data. This helps ensure that our
decision is data driven rather than
being made by emotional factors to get the outcome we want
rather than the outcome that fits the
data. (Much like making our detectives go after the suspect that
did the crime rather than the one
they do not like and want to arrest, at least when they are being
honest detectives.)
The decision rule for our class is very simple, and will always
be the same:
Reject the null hypothesis if the p-value is less than our alpha
of .05. (Note: this would
be the same as saying that if the p-value is not less than 0.05,
we would fail to reject the null
hypothesis.)
We introduced the p-value last week, it is the probability of our
outcome being as large or
larger than we have by pure chance alone. The further from the
actual mean a sample mean is,
the less chance we have of getting a value that differs from the
mean that much or more; the
closer to the actual mean, the greater our chance would be of
getting that difference or more
purely by sampling error.
Our decision rule ties our criteria for significance of the
outcome, the step 2 choice of

22. alpha, with the results that the statistical tests will provide (and,
the Excel tests will give us the p-
values for us to use in making the decisions).
These four steps define our analysis, and are done before we do
any analysis of the data.
Step 5
Once we know how we will analyze and interpret the results, it
is time to get our sample
data and set it up for input into an Excel statistical function.
Some examples of how this data
input works will be discussed in the third lecture for this week.
This step is fairly easy, simply identify the statistical test we
want to use. The test to use
is based on our question and the related hypothesis claims. For
this week, if we are looking at
variance equality, we will use the F-test. If we are looking at
mean equality, we will use the T-
test.
Step 6
Here is where we bring everything together and interpret the
outcomes.
What is constant about this step is the need to:
1. Look at the appropriate p-value (indicated in the test outputs,
as we will see in lecture
2).

23. 2. Compare the p-value with our value for alpha (0.05).
3. Make a decision: if the test p-value is less than or equal to
(<=) 0.05, we will reject
the null hypothesis. If the test p-value is more than (=>) 0.05,
we will fail to reject
the null hypothesis.
Rejecting the null hypothesis means that we feel the alternate
hypothesis is the more
accurate statement about the populations we are testing. This is
the same for all of our statistical
tests.
Once we have made our decision to reject or fail to reject the
null hypothesis, we need to
close the loop, and go back and answer our original question.
We need to take the statistical
result or rejecting or failing to reject the null and turn it into an
“English” answer to the question.
Doing so depends on how the original question lead to the
hypothesis statements. Examples of
this follow in Lecture 2.
Lectures 2 and 3 will show how to use this process in
conjunction with Excel and the F
and T tests. For now, focus on the logic of setting up the
testing instructions.
Summary
This week we begin our journey discovering ways to make
decisions on data, and more
specifically differences in data sets, based on generally agreed
upon approaches rather than by
“guess and by golly.” The process is called hypothesis testing

24. and is part of the scientific
method of research and decision making.
In this approach we always test a claim of no difference (the
null hypothesis) whether or
not we are suspect or desire to see an actual difference. The
null hypothesis is paired with an
alternate hypothesis that is exactly the opposite claim.
Decisions are made based on a p-value
which is the probability that we would see a difference as large
or larger as we got if the null
hypothesis is true. Small p-values mean we reject the null as
not being an accurate description of
the population we are looking at.
The hypothesis testing process (or procedure) has six steps.
The first four are completed
before we look at the data; the fifth step is the actual
calculation of the statistical test and the
final and sixth step is where the analysis of the results is done.
The steps are:
1. State the null and alternate hypothesis
2. Select a level of significance
3. Identify the statistical test to use
4. State the decision rule
5. Perform the analysis
6. Interpret the result
If you have any questions on this material, please ask your
instructor.

25. After finishing with this lecture, please go to the first
discussion for the week and engage
in a discussion with others in the class over the first couple of
days before reading the second
lecture.
CASE 31 Hereditary
Angioedema
Regulation of complement activation.
Complement is a system of plasma proteins that participates in a
cascade of
reactions, generating active components that allow pathogens
and immune
complexes to be destroyed and eliminated from the body.
Complement is part
of the innate immune defenses of the body and is also activated
via the anti-
bodies produced in an adaptive immune response. Complement
activation
is generally confined to the surface of pathogens or circulating
complexes of
antibody bound to antigen.
Complement is normally activated by one of three routes: the
classical path-
way, which is triggered by antigen:antibody complexes or
antibody bound
to the surface of a pathogen; the lectin pathway, which is

26. activated by
mannose-binding lectin (MBL) and the ficolins; and the
alternative pathway,
in which complement is activated spontaneously on the surface
of some
bacteria. The early part of each pathway is a series of
proteolytic cleavage
events leading to the generation of a convertase, a serine
protease that
cleaves complement component C3 and thereby initiates the
effector actions
of complement. The C3 convertases generated by the three
pathways are
different, but evolutionarily homologous, enzymes. Complement
compon-
ents and activation pathways, and the main effector actions of
complement,
are summarized in Fig. 31.1.
The principal effector molecule, and a focal point of activation
for the system,
is C3b, the large cleavage fragment ofC3. If active C3b, or the
homologous but
less potent C4b, accidentally becomes bound to a host cell
surface instead of
a pathogen, the cell can be destroyed. This is usually prevented
by the rapid
hydrolysis of active C3b and C4b if they do not bind
immediately to the sur-
face where they were generated. Protection against
inappropriate activation
of complement is also provided by regulatory proteins.
One of these, and the most potent inhibitor of the classical
pathway, is the
C1 inhibitor (ClINH) . This belongs to a family of serine

27. protease inhib-
itors (called serpins) that together constitute 20% of all plasma
proteins. In
addition to being the sole known inhibitor of C1, CIINH
contributes to the
I Topics bearing on
this case:
Classical pathway of
complement activation
Inhibition of C1
activation
Alternative pathway of
complement activation
-
Inflammatory effects of
complement activation
Regulation of C4b IThis case was prepared by Raif Geha, MD,
in collaboration vvith Arturo Borzutzkyr, MD.
3 Case 31: Hereditary Angioedema
Fig. 31.1 Overview of the main
components and effector actions of
complement. The early events of all
three pathways of complement activation
involve a series of cleavage reactions
that culminate in the formation of an
enzymatic activity called a C3 convertase,
which cleaves complement component

28. C3 into C3b and C3a. The production of
the C3 convertase is the point at which
the three pathways converge and the
main effector functions of complement are
generated. C3b binds covalently to the
bacterial cell membrane and opsonizes
the bacteria, enabling phagocytes
to internalize them. C3a is a peptide
mediator of local inflammation. C5a
and C5b are generated by the cleavage
of C5b by a C5 convertase formed by
C3b bound to the C3 convertase (not
shown in this simplified diagram). C5a
is also a powerful peptide mediator of
inflammation. C5b triggers the late events
in which the terminal components of
complement assemble into a membrane-
attack complex that can damage the
membrane of certain pathogens. Although
the classical complement activation
pathway was first discovered as an
antibody-triggered pathway, it is now
known that C 1 q can activate this pathway
by binding directly to pathogen surfaces,
as well as paralleling the lectin activation
pathway by binding to antibody that is
itself bound to the pathogen surface.
In the lectin pathway, MASP stands
for man nose-binding lectin-associated
serine protease.
CLASSICAL PATHWAY
Antigen:antibody complexes
(pathogen surfaces)

29. .,,-l 7
C1q, Clr, Cls
C4
C2
~
J~
C3a, CSa
.,,-l 7
Peptide mediators
of inflammation,
phagocyte recruitment
LECTIN PATHWAYI I
Mannose-binding lectin or
ficolln binds carbohydrate
on pathogen surfaces
.,,-l 7
MBUficolin, MASP-2

30. C4
C2
~ 7
C3 convertase
~ l
C3b
.,,-l 7
Binds to complement
receptors on phagocytes
~ 'z
Opsonization
of pathogens
Removal of
immune complexes
I I ALTERNATIVE PATHWAY
Pathogen surfaces
.,,-l 7
C3
B
0
)

31. ~ T"miMIcomplement components
CSb
C6
C7
C8
C9
.,,-l 7
Membrane-attack
complex,
lysis of certain
pathogens and cells
regulation of serine proteases of the clotting system and of the
kinin system,
which is activated by injury to blood vessels and by some
bacterial toxins. The
main product of the kinin system is bradykinin, which causes
vasodilation and
increased capillary permeability.
ClINH'intervenes in the first step of the complement pathway,

32. when CI binds
to immunoglobulin molecules on the surface of a pathogen or
antigen:antihody
complex (Fig. 31.2). Binding of two or more of the six tulip-
like heads of the
Clq component of CI is required to trigger the sequential
activation of the two
associated serine proteases, Clr and CIs. ClINH inhibits both of
these pro-
teases, by presenting them with a so-called bait -site, in the
form of an arginine
bond that they cleave. !lJhen Clr and CIs attack the bait-site
they covalently
bind CIINH and dissociate from Clq. By this mechanism, the Cl
inhibitor lim-
its the time during which antibody-bound CI can cleave C4 and
C2 to generate
C4b2a. the classical pathway C3 convertase.
Activation of CI also occurs spontaneously at low levels
vvithout binding to
an antigen:antibody complex, and can be triggered further by
plasmin, a pro-
tease of the clotting system, which is also normally inhibited by
Cl1NH. In
the absence of ClINH, active components of complement and
bradykinin are
produced. This is seen in hereditary angioedema (HAE), a
disease caused by a
genetic defiCiency of C lINH.
Case 31: Hereditary Angioedema ~
o

33. Cl q binds to IgM on bacterial surface Clq binds to at least two
IgG molecules
on bacterial surface
r C11NH
o
Binding of Cl q to Ig activates Cl r, which cleaves
and activates the serine protease Cl s
Cl lNH dissociates Clr and Cls Irom the active Cl complex
:B C1s r C1 1NH
C1r
o o
:BC1S
Fig. 31.2 Activation of t he classical
pathway of complement and
intervention by C1INH. In the left panel,
one molecule of IgM, bent into the
'staple' conformation by binding several
identical epitopes on a pathogen surface,
allows binding by the globular heads
of C1 q to its Fc pieces on the surface
of the pathogen . In the right panel,
multiple molecules of IgG bound to the
surface of the pathogen allow binding
by C1 q to two or more Fc pieces. In
both cases, binding of C 1 q activates the
associated C 1 r, whi<;h becomes an active
enzyme that cleaves the proenzyme

34. C1 s, a serine protease that initiates the
classical complement cascade. Active
C1 is inactivated by C1INH, which binds
covalently to C1 rand C1 s, causing them
to dissociate from the complex. There are
in fact two C1 r and two C1 s molecules
bound to each C1 q molecule, although for
simplicity this is not shown here. It takes
four molecules of C11NH to inactivate all
the C1 rand C1 s.
The case of Richard Crafton: afailureof
communication as well as of complement regulation.
Richard Crafton was a17-year-old hIgh-school senior when he
had an attack of severe
abdominal pain at the end of a school day. The pain came as
frequent sharp spasms
and he began to vomit. After 3 hours, the pain became
unbearable and he went to the
emergency room at the local hospital.
At the hospital, the intern who examined him found no
abnormalities other than dry
mucous membranes of the mouth, and atender abdomen.There
was no point tender-
ness to indicate appendicitis. Richard continued to vomit every
5 minutes and said
the pain was getting worse.
A surgeon was summoned. He agreed with the intern that
Richard had an acute
abdominal condition but was uncertain of the diagnosis. Blood
tests showed an
elevated red blood cell count, indicating dehydration. The
surgeon decided to pro-

35. ceed with exploratory abdominal surgery. A large midline
Incision revealed a moder-
ately swollen and pale jejunum but no other abnormalities were
noted. The surgeon
removed Richard's appendix, which was normal, and Richard
recovered and returned
to school 5 days later.
What Richard had not mentioned to the intern or to the surgeon
was that, although he
had never had such severe pains as those he was experiencing
when he went to the
~ Case 31: Hereditary Angioedema
Fig. 31.3 Hereditary angioedema.
Transient localized swelling that occurs in
this condition often affects the face.
emergency room, he had had episodes of abdominal pain since
he was 14 years old.
No one in the emergency room asked him if he was taking any
medication, or took a
family history or a history of prior illness. If they had, they
would have learned that
Richard's mother, his maternal grandmother,and amaternal
uncle, also had recurrent
episodes of severe abdominal pain, as did his only sibling, a 19-
year-old sister.
As a newborn,Richard was prone to severe colic. When he was 4
years old, abump on
his head led to abnormal swelling. When he was 7, a blow with
a baseball bat caused

36. his entire left forearm to swell to twice its normal size. In both
cases, the swelling was
not painful, nor was it red or Itchy, and it disappeared after
2days. At age 14 years,he
began to complain of abdominal pain every few months,
sometimes accompanied by
vomiting and,more rarely, by clear, watery diarrhea.
Richard's mother had taken him at age 4years to an
immunologist, who listened to the
family history and immediately suspected hereditary
angioedema. Thediagnosis was
confirmed on measuring key complement components. C11NH
levels were 16% of the
normal mean and C41evels were markedly decreased, while C3
levels were normal.
When Richard turned up for a routine visit to his immunologist
a few weeks after his
surgical misadventure, the immunologist, noticing Richard's
large abdominal scar,
asked what had happened. When Richard explained, he
prescribed daily doses of
Winstrol (stanozolol). This caused a marked diminution in the
frequency and sever-
ity of Richard's symptoms. When Richard was 20 years old,
purified C11NH became
available; he has since been infused intravenously on several
occasions to alleviate
severe abdominal pain, and once for swelling of his uvula,
pharynx, and larynx. The
Infusion relieved his symptoms within 25 minutes.
Richard subsequently married and had two children. The C11NH
level was found to be
normal in both newborns.

37. Hereditary angioedema.
Individuals like Richard with a hereditary deficiency of ClINH
are subject
to recurrent episodes of circumscribed swelling of the skin (Fig.
31.3), intes-
tine, and airway. Attacks of subcutaneous or mucosal swelling
most com-
monly affect the extremities, but can also involve the face,
trunk, genitals,
lips, tongue, or larynx. Cutaneous attacks cause temporary
disfigurement but
are not dangerous. When the swelling occurs in the intestine it
causes severe
abdominal pain, and obstructs the intestine so that the patient
vomits. When
the colon is affected, watery diarrhea may occur. Swelling in
the larynx is the
most dangerous symptom, because the patient can rapidly choke
to death.
HAE attacks do not usually involve itching or hives, which is
useful to differen-
tiate this disease from allergic angioedema. However, a
serpiginous, or linear
and wavy, rash is sometimes seen before the onset of swelling
symptoms. Such
episodes may be triggered by trauma, menstrual periods,
excessive exercise,
exposure to extremes of temperature, mental stress, and some
medications
such as angiotensin-converting enzyme inhibi tors and oral
contraceptives.
HAE is not an allergic disease, and attacks are not mediated by
histamine.

38. .HAE attacks are associated with activation of four serine
proteases, which are
normally inhibited by ClINH. At the top of this cascade is
Factor XlI , which
directly or indirectly activates the other three (Fig. 31.4) .
Factor XlI is normally
activated by injury to blood vessels, and initiates the kinin
cascade, activating
Case 31: Hereditary Angioedema ~
Fig. 31.4 Pathogenesis of hereditary angioedema. Activation of
Factor XII leads to
the activation of kallikrein, which cleaves kininogen to produce
the vasoactive peptide
bradykinin; nalso leads to the activation of plasmin, which in
turn activates C1. C1
cleaves C2, whose smaller fragment C2b is further cleaved by
plasmin to generate the
vasoactive peptide C2 kinin. The red bars represent inhibition
by C1INH.
kallikrein, which generates the vasoactive peptide bradykinin.
Factor XII also
indirectly activates plasmin, which, as mentioned earlier,
activates C1 itself.
Plasmin also cleaves C2b to generate a vasoactive fragment
called C2 kinin. In
patients deficient in ClINH, the uninhibited activation of Factor
XII leads to
the activation of kallikrein and plasmin; kallikrein catalyzes the
formation of
bradykinin, and plasmin produces C2 kinin. Bradykinin is the
main mediator

39. responsible for HAE attacks by causing vasodilation and
increasing the per-
meability of the postcapillary venules by causing contraction of
endothelial
cells so as to create gaps in the blood vessel wall (Fig. 31.5).
This is responsible
for the edema; movement of fluid from the vascular space into
another body
compartment, such as the gut, causes the symptoms of
dehydration as the
vascular volume contracts.
Treatment of HAE can focus on preventing attacks or on
resolving acute epi-
sodes. Purified or recombinant ClINH is an effective therapy in
both these
settings. A kallikrein inhibitor and a bradykinin receptor
antagonist have also
been developed to target the kinin cascade and bradykinin
activity.
Questions.
mActivation of the complement system results in the release of
histamine and chemokines, which normally produce pain, heat,
and itching.
Why is the edema Auid in HAE free of cellular components, and
why does
the swelling not itch?
QJ Richard has a markedly decreased amount of C4 in his
blood. This is
because it is being rapidly cleaved by activated C1. What other
complement
component would you expect to find decreased? Would you
expect the

40. alternative pathway components to be low, normal, or elevated?
What
about the terminal components?
Fig. 31.5 Contraction of endothelial cells creates gaps in the
blood vessel wall.
A guinea pig was injected intravenously with India ink (a
suspension of carbon particles).
Immediately thereafter the guinea pig was injected
intradermally with a small amount
of activated C1 s. An area of angioedema formed about the
injected site, which was
biopsied 10 minutes later. An electron micrograph reveals that
the endothelial cells in
post-capillary venules have contracted and formed gaps through
which the India ink
particles have leaked from the blood vessel. L is the lumen of
the blood vessel; P is a
polymorphonuclear leukocyte in the lumen; rbc is a red blood
cell that has leaked out of
the blood vessel. Micrograph courtesy of Kaethe Willms.
Activation of Factor XII
Activation of
kallikrein
Cleavage of kininogen
to generate bradykinin,
vasoactive peptide
Activation of
proactivator