powerpoint - principles of experimental design and data analysis
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Powerpoint preserntation - Principles of Experimental Design and Data AnalysisTRANSCRIPT
Presentation Title Goes Here
…presentation subtitle.Principles of Experimental Design
Violeta Bartolome
Senior Associate Scientist-Biometrics
Crop Research Informatics Laboratory
International Rice Research Institute
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What is an experiment?
Experiment is an activity undertaken to solve a
particular problem or question, to retain or falsify a
hypothesis concerning a phenomenon.
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Design of an Experiment
• Includes the set of treatments selected for comparison
• Specifies the subjects or experimental units to which the
treatments will be applied
• Gives the rules by which the treatments are allocated to the
experimental units
• Indicates the measurements to be made
• Indicates the analyses to be done to meet the objectives
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Objective of experimental design
To ensure that measurements made on experimental
units are:
1. Free from bias
2. Precise
3. Scientifically valid – valid experimental designs
and procedures
Forward
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Bias
The consistent deviation
of analytical results from
the "true" value caused
by systematic errors in a
procedure.
back
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Precision
The closeness with which results of replicate
analyses of a sample agree. It is a measure of
dispersion or scattering around the mean value
Biased, high precision Unbiased, low precision Unbiased, high precision
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Two sets of data:
Set I – 2 5 12 25
Set II - 9 13 12 10
Which set would have a more precise
estimate of the mean?
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Precision is achieved through
• Uniform experimental units
• Careful conduct of all operations before and during
the experiment
• More replicates
• Appropriate experimental design
back
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Experimental Unit
• Unit of experimental material to which a treatment is
applied.
• Example: single leaf, a whole plant, an area of land
containing many plants, a pot or a bench in the
greenhouse, a single animal, several animals, or an
entire herd.
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Treatment
• Dosage of material or a method that is to be tested in
the experiment.
• Example: crop variety, fertilizer level, or a
management practice.
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Factors and levels of treatments
• Factors - variables that the experimenter varies in the experiment
• Levels - various quantities or aspects of a given factor
Factors Levels
variety IR64, Peta, IR8
fertilizer 0, 30, 60, 90 N kg/ha
water stress well watered, stressed
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Control
• Included in the experiment to assess what would
happen if the experimental manipulation had not
been performed
• Example: no fertilizer application, no weed control,
farmer’s variety
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Treatment effect
expected increase or decrease in response to treatment application
Variety Varietal effect on yield
Hybrid 6 t/ha
Local 3 t/ha
varietal effect is the
“increase” in yield of the
hybrid over the local check
Pesticide application Effect on insect population
without 75
with 10
treatment effect is the
“decrease” in insect
population when pesticide is
applied
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Choice of treatment
• Dependent on questions researcher wants to be
answered.
• May be chosen at random from all possible levels of
application or purposely selecting levels to be
included.
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Experiment: Compare Variety A and Variety B
Hypothesis: Yield of new rice Variety A will be higher
than the traditional Variety B.
Objective: To test the hypothesis that the new rice
Variety A will out yield the traditional
Variety B.
How should this be done?
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With all inputs the same, can you say that Variety A is
higher yielding than Variety B?
Variety A Variety B
4 tons 3.5 tons
H20
H20N N
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Will you get the same yield?
Variety A Variety A
Chances are you will
not get the same yield
because of random or
experimental error or
unexplained variation.
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What is Experimental Error
• Not an error in the sense of being wrong
• Defined as the differences among experimental units
treated alike
• It can be minimized but never totally eliminated.
There will always be some variations that cannot be
controlled
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When is a treatment effect significant?
4.0
tons3.5
tons
A B
4.0
tons4.1
tons
A A
D = yield difference
between A and B
(treatment effect)
D = 0.5
E = yield difference
between same variety
(experimental error)
E = 0.1
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When is “D”, the observed yield
difference, considered significant?
-- if D is substantially greater than E, then D is
significant
Subjective Objective
(no bias)
Statistical tests
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SmallLARGE
ERRORChance of detectingtreatment difference
SmallLARGE
Effect of experimental error in
statistical tests
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How do you measure experimental error?
By Replication
• repeat treatment two or more times
• have a more precise estimate of treatment
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An experiment involving 4 water management treatments in 3 replications is to be conducted. Layout is shown below:
At harvest, the plot is divided into 3 parts to correspond to 3
replications as shown below:
Are these “true” replications?
8m
T4 T3 T1
9m
T2
9m
8m
T2 T4 T3 T1
NO! replicates are
not independent.
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CRD
RCB
T2 T1 T4 T2
T1 T3 T1 T4
T3 T4 T3 T2
9m
8m
T4 T3
T3
T1 T2
T1 T2 T4
T2 T1 T4 T3
Rep I
Rep II
Rep III
9m
8m
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• Plots planted to A have a higher level of productivity than plots planted to B.
• Comparison between yield performances of A and B would be biased in favor of A.
• To avoid bias treatments should be randomized.
B A
B A
fertility gradient
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Randomization
• assignment of treatments to experimental plots so
that experimental units have an equal chance of
receiving any treatment
• ensures unbiased estimate of treatment means and
experimental error
• assures validity of the statistical tests
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REPLICATION
• provides an estimate of the experimental error
RANDOMIZATION
• ensures that the estimates are unbiased
• ensures validity of statistical tests
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Error Control
Ways to minimize or reduce experimental error?
o Increase number of replication
o Blocking
o Use of appropriate experimental design
o Rigorous control of measurement and field plot techniques
end
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Blocking
assigning similar or uniform experimental units
into a “block”
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Direction of source of variation is
known
High
LowBlock1
Block2
Block3
Block4
Block perpendicular to the source of variation
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Direction of source of variation is
unknown
Block1Block1 Block2Block2
Block3Block3 Block4Block4
• Use compact blocks
• Avoid using long and narrow blocks
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Direction of
water
gradient
Direction of fertility gradient
Two known sources of variation in perpendicular directions
back
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Proper experimental design must include
• Replication
• Randomization
• Error Control