Introduction to experimental design

EXPERIMENTAL
DESIGN
Dr. Rasha Aly Elsayed1 & Dr. Sanaa Abd Eltawab2
1Al Azhar University 2Beni Suef University

2nd Lecture

Intended learning outcomes
2

 Define statistics.
 Define Experimental Design.
 Know the Importance of Experimental Design.
 Identify the Relationships between Experimental Design
and Statistics.
 Identify Some Myths about Experimental Design.
 Briefly Describe the Costs of Poor Experimental design.
 Steps in good experimental design
 Goals of Experimental Design.

Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab

Biological research involves data!!
3

1) Collecting
Data
 Experimental Design
2) Summarizing Data
 Simple numerical and graphical descriptions
3) Analyzing Data
 Formal statistical methods for hypothesis testing and
estimation
4) Communicating Results
 Discussion and Interpretation


What is statistics?
4

 Statistics: A collection of procedures and processes to
enable researchers in the unbiased pursuit of Knowledge.
.‫مجموعة من الطرق والعمليات تمكن الباحثين من السعى وراء المعلومه بال تحيز‬

 Statistics is an important part of the Scientific Method.

State a Hypothesis

Interpret the Design a
Results—Draw Study and
Conclusions Collect Data
Analyze the Data


What is statistics?
5

the core help from the statistician is in the design of the
experiment

Help with selecting conditions that relate to the
objectives of the study

Selecting the Experimental Units

Deciding when REPLICATIONS exist
Determining the ORDER in which the experiment is to
be carried out

THE DESIGN OF THE EXPERIMENT IS CRITICAL

What Is Experimental Design?
6

 Experimental design is the part of statistics that
happens before you carry out an experiment.
 Science answers questions with experiments.

 Efficient and Effective Experiments Maximizing
Information with Limited Resources.


What Is Experimental Design?
7

 Biological insight!
 Logic

 Common sense
 Planning

 Requires an appreciation of statistics

Note that there are different approaches to
Experimental Design.

Experimental Design and Statistics
8

 Good experimental design is about more than
statistics.

 You MUST know how you will analyse your
experiment before you collect a single datum!

 Once you have designed your experiment seek
advice on the statistical test you will use.

 Go ahead and use experienced people in your lab
or department and/or a expert in statistics for this.


Some myths about Experimental
Design
9

 Myth 1
Its better to spend time collecting data than
sitting around thinking about collecting data,
just get on with it.

 Reality
A well designed experiment will save you tons of
time. This belief often results in staff and post-docs
sitting around while supervisors rewrite grant
proposals and permit applications


Design
10

 Myth 2
“It does not matter how you collect your data, there
will always be a statistical ‘fix’ that will allow you to
analyze them”.

 Reality
NO! This belief results in people having lots of
problems with their data. Big problems are non-
independence and lack of control groups.


11
Design

 Myth 3
“If you collect lots of data something interesting will
come out and you will be able to detect even very
subtle effects”

 Reality
NO! Generally collecting lots of data without a plan
wastes your time and someone’s money.


Costs of poor design
12

 Time is wasted
This is something you can’t afford and its sometimes
downright embarrassing.

 Money and resources are wasted
This is something your supervisor (or department or
company) can’t afford and tends to make them quite
angry.


Costs of poor design
13

 Ethical issues (when animals or humans are
experimental subjects)
Experiments must minimize the stress and suffering of
any animals involved.
Minimum numbers must be used.
Experiments must have a reasonable chance of
success.
Ethical issues include causing damage or excessive
disturbance to an ecosystem.
Using poor design in animal studies is not only wasteful
and embarrassing but may also be illegal.

Steps in good experimental design
14

Three important steps in good experimental design:
1. Define the objectives: Record (i.e. write down) precisely what you
want to test in an experiment.

2. Devise a strategy: Record precisely how you can achieve the
objective. This includes thinking about the size and structure of the
experiment - how many treatments? how many replicates? how will
the results be analysed?

3. Set down all the operational details: How will the experiment be
performed in practice? In what order will things be done? Should the
treatments be randomized or follow a set structure? Can the
experiment be done in a day? Will there be time for lunch? etc.


Goals of Experimental Design
15

 I. Avoid experimental artifacts
 II. Eliminate bias
1. Use a simultaneous control group
2. Randomization
3. Blinding
 III. Reduce sampling error
1. Replication
2. Balance
3. Blocking

I. Experimental Artifacts
16

 Experimental artifacts:
a bias in ‫انحياز‬ a
measurement produced by unintended ‫مقصود‬ ‫غير‬

consequences of experimental procedures.

 Conduct your experiments under as natural of
conditions as possible to avoid artifacts.


II. Eliminate bias: 1. Control Group
17

 A control group is a group of subjects left
untreated for the treatment of interest but
otherwise experiencing the same conditions
as the treated subjects.

 Example: one group of patients is given an inert
placebo (inert medication).


II. Eliminate bias: The Placebo Effect
18

 Patients treated with placebos, including
sugar pills, often report improvement.
 Example: up to 40% of patients with chronic
back pain report improvement when treated
with a placebo.
 Even “sham surgeries” can have a positive
effect.

This is why you need a control group!


II. Eliminate bias: 2. Randomization
19

 Randomization is the random assignment of
treatments to units in an experimental study.

 Breaks the association between potential
confounding variables and the explanatory
variables.


II. Eliminate bias: 3. Blinding
20

 Blinding is the concealment of information ‫اخفاء‬

from the participants and/or researchers about
which subjects are receiving which treatments.

 Single blind: subjects are unaware of
treatments.
 Double blind: subjects and researchers are
unaware of treatments.


II. Eliminate bias: 3. Blinding
21

 Example: testing heart medication
 Two treatments: drug and placebo
 Single blind: the patients don’t know which
group they are in, but the doctors do.
 Double blind: neither the patients nor the
doctors administering the drug know which
group the patients are in.


III. Reduce sampling: 1. Replication
22

 This is the number of experimental units measured for each treatment. Increasing
the number of replications means collecting more information about the treatments.

 Experimental unit: the individual unit to which treatments are assigned

2 Experimental
Experiment 1
Units

Pseudo replication
2 Experimental
Experiment 2
Units
Tank 1 Tank 2

8 Experimental
Units Experiment 3
All separate tanks

III. Reduce sampling:1. Replication
Why is pseudoreplication bad?
23

Experiment 2

Tank 1 Tank 2

 problem with confounding and replication!
 Imagine that something strange happened, by
chance, to tank 2 but not to tank 1
 Example: light burns out
 All four lizards in tank 2 would be smaller
 You might then think that the difference was due
to the treatment, but it’s actually just random
chance Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab

III. Reduce Sampling.1. Replication

24

Why is replication good?
 Consider the formula for standard error of the
mean:
s
SE Y 
n
Larger n Smaller SE

III. Reduce sampling: 2. Balance
25

 In a balanced experimental design, all treatments
have equal sample size.

Better than

Balanced Unbalanced
 This maximizes power.
 Also makes tests more robust to violating
assumptions.

III. Reduce sampling: 3. Blocking
26

 Blocking is the grouping of experimental units
that have similar properties.
 Within each block, treatments are randomly
assigned to experimental treatments
 Blocking allows you to remove extraneous
variation from the data.
 Like replicating the whole experiment multiple
times, once in each block.
 Paired design is an example of blocking.

27

Experiments with 2 Factors
 Factorial design – investigates all treatment
combinations of two or more variables.
 Factorial design allows us to test for interactions
between treatment variables


28

Factorial Design
pH
5.5 6.5 7.5  An interaction between
Temperature

two (or more) explanatory
25 n=2 n=2 n=2 variables means that the
30 n=2 n=2 n=2 effect of one variable
depends upon the state
35 n=2 n=2 n=2 of the other variable

40 n=2 n=2 n=2


Thank You
29

THANK YOU


Introduction to experimental design

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