Powerpoint - Principles of Experimental Design and Data Analysis

Presentation Title Goes Here

…presentation subtitle.Principles of Experimental Design

Violeta Bartolome

Senior Associate Scientist-Biometrics

Crop Research Informatics Laboratory

International Rice Research Institute

:: color, composition, and layout

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.


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


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


Bias

The consistent deviation

of analytical results from

the "true" value caused

by systematic errors in a

procedure.

back


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


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?


Precision is achieved through

• Uniform experimental units

• Careful conduct of all operations before and during

the experiment

• More replicates

• Appropriate experimental design

back


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.


Treatment

• Dosage of material or a method that is to be tested in

the experiment.

• Example: crop variety, fertilizer level, or a

management practice.


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


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


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


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.

:: color, composition, and layout15

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?


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


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.


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


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


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


SmallLARGE

ERRORChance of detectingtreatment difference

SmallLARGE

Effect of experimental error in

statistical tests


How do you measure experimental error?

By Replication

• repeat treatment two or more times

• have a more precise estimate of treatment


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.


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


• 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


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


REPLICATION

• provides an estimate of the experimental error

RANDOMIZATION

• ensures that the estimates are unbiased

• ensures validity of statistical tests


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


Blocking

assigning similar or uniform experimental units

into a “block”


Direction of source of variation is

known

High

LowBlock1

Block2

Block3

Block4

Block perpendicular to the source of variation


Direction of source of variation is

unknown

Block1Block1 Block2Block2

Block3Block3 Block4Block4

• Use compact blocks

• Avoid using long and narrow blocks


Direction of

water

gradient

Direction of fertility gradient

Two known sources of variation in perpendicular directions

back


Proper experimental design must include

• Replication

• Randomization

• Error Control

Documents

Powerpoint - Principles of Experimental Design and Data Analysis