Here is a piece of detailed information about the experimental design used in the field of statistics. This also features some information on the three most widely accepted and most widely used designs.

1. EXPERIMENTAL
DESIGN
Maira Jabeen
L1F18BSFT0021

2. What is Design of
Experiment?
Experimental design is a study in statistics which deals with the design
and analysis of experiments. It is a way to carefully plan experiments
in advance so that your results are both valid and objective.
“ “

3.  Sir Ronald Fischer introduced this system of
experimental design in 1935.
 He is a well known statistician, eugenicist,
evolutionary biologist, geneticist.
Founder of Experimental Design
HE IS KNOWN AS THE FATHER OF MODERN STATISTICS.

4. Importance of Experimental
Design.
 Experimental designs are important for determining the cause and
effect relationship between dependent variable and independent
variable.
 It is a statistical method in which a researcher plans to observe
effect of desired factors on response.

5. Design of Experiment Includes;
01
02
03
04
The systematic collection of data.
A focus on the design itself, it should be easy and provide
with the most efficient and best results.
Planning changes to independent variables (input) and the effect
on dependent variables also known as response variables.
A good experimental design ensures that the results are
valid, easily interpreted, and definitive.

6. Principles of
Experimental Design
Randomization
Replication
Blocking

7. DESIGNING AN EXPERIMENT
Following parameters must be kept in consideration.
$75
DETERMINE
THE
NEED
FOR SAMPLE
$80
POPULATION
OF
INTEREST
$75
DEFINING
THE
PROBLEMS
AND
QUESTION

8. An independent variable is the one
which is controlled or changed in a
scientific experiment to examine the
effects on the dependent variable.
A dependent variable is the one
which is being measured & tested
in a scientific experiment, under
specific conditions.
Variables Used in
Experimental Design
Dependent
Variable
Independent
Variable

9. Steps Involved In Experimental Designing
06
Defining problems and
questions.
Determining the
objectives
Formulation of
hypothesis.
Analyze and interpret the
Data.
Drawing
Conclusions
01
02
03
05
06
Execution of
Experiment.
04
Brainstorming Verify the predicted
results.
08
07

10. FOR EXAMPLE
If we examine any plant field and the growth of plants then the problem is that if the plant
grows more by the use of fertilizers or not?
Does cause/affect ?
 Independent Variable: Fertilizer
 Dependent Variable: Plant Growth
Example Hypothesis:
The plant treated with fertilizer will grow larger than the plant grown without fertilizer. The
independent variable here is the use of fertilizer while the dependent variable is the growth
rate of plant.

11. Prediction of
Results
Execution of
Experiment
Analyzing
Results
Drawing
Conclusions
The hypothesis clearly in
dicates and predicts the
result that there will be o
ne group of plants that
will grow larger than the
other group of plants.
The experiment is then
executed and both the group
of plants are observed. Whil
e providing one group with
fertilizer and other group
growing naturally.
In this example the conclu
sion will be as follows tha
t the group of plants whic
h shows more growth is th
e one which had given a
good dose of fertilizer.
All the results that is the gr
owth of plants is then analy
zed by the researcher and o
bserved carefully. This step
marks the interpretation of
results of the experiment.
AFTER HYPOTHESIS…..

12. MOST WIDEY
USED
EXPERIMENTAL
DESIGNS
CRD
Completely
Randomized
Design
FED
Factorial
experimental
Design
RBD
Randomized
Block
Design

13. COMPLETELY
RANDOMIZED
DESIGN

14. DISADVANTAGES
• Less precise
• less valid results
• Chances of error are more
ADVANTAGES
• Flexibility
• One way analysis
• Experimental units at random.
COMPLETELY RANDOMIZED DESIGN
• CRD is the most simplest based on the randomization and replication.
• In CRD all treatments are allocated randomly among the experimental factors.
• This involves every experimental unit to have an equal probability of receiving
a treatment.

15. Implementation of CRD
A CRD, completely randomized design is generally implemented by:
Listing the treatment levels or treatment
combinations.
Assigning each level a random number.
Sorting the random numbers in order, to
produce a random application order for the
treatments
01
02
03

16. EXAMPLE OF CRD

17. FACTORIAL
EXPERIMENTAL
DESIGN

18. FACTORIAL EXPERIMENTAL DESIGN
• A factorial experimental design is the one which is
used to investigate the effect of two or more indepe
ndent variables on one dependent variable.
• Factorial experimental design is used to draw concl
usions about more than one factor, or variable.
• The term factorial itself is used to indicate that all
possible combinations of the factors are considered
in this experimental design.
Importance of Factorial
Design
• Use of factorial experiments enab
les us to examine and determine
one-factor at a time.
• These in return provides us with t
he most efficient results and the e
ffects of possible interactions bet
ween several factors named as in
dependent variables.

19. EXAMPLE
Investigate a research work to examine the components for increasing SAT Scores.
For the investigation on following work we need the following three components.
The above mentioned values are the independent variables. Each of the independent variables
is termed as a factor, and each factor comprises of two levels (yes or no).
As this experiment consists of 3 factors with 2 levels, this is a 2 x 2 x 2 = 23 factorial design.
An experiment with 3 factors and 3 levels would be a 33 factorial design.
And an experiment with 2 factors and 3 levels would be a 32 factorial design.
SAT intensive class
(yes or no).
SAT Preparation book
(yes or no).
Extra homework
(yes or no).

20. RANDOMIZED
BLOCK
DESIGN

21. RANDOMIZED BLOCK DESIGN
• In randomized block design, the
experimental subjects are divided into
homogeneous blocks by the
researcher
• And then the treatments are assigned
randomly to them.
• In a good randomized block designed,
the variability within blocks should
always be greater than the variability
between blocks.
Advantages of Randomized
Block Design
• Most efficient band best results without
any variability.
• No restriction on the number of treatments
• Elimination of missing slots is very
convenient.
• No restriction on the replication of
treatments.

22. EXAMPLE
According to the Merck Manual, one factor which can greatly affect the way how a patient responds
to a drug is his/her age. Therefore, we have the risk that the results you are getting might be affected
by age as a confounding variable.
This randomized block design shown above in the image is containing equal blocks of 200 people
from each age group. Where these people are assigned randomly to either the real drug or the plac
ebo. Therefore, in this experiment age is removed as a potential source of variability.
While considering this experimental example we can also say that age is not the only potential so
urce of bring a variability in the experiment.

23. Thank you