1. Experimental Design Basics

2. What is an Experiment
Process that deliberately imposes a treatment on a group of experimental units/subjects in the interest of observing the response
Purpose is to prove a cause-and-effect relationship
Treatment – a specific condition that researchers administer to subjects
Basic experimental design
subjects  treatment  observation
Ex:
A doctor treats a patient with a skin condition with different creams to see which is most effective.

3. Treatments
Factors
Controlled independent variable whose levels are set by the experimenter
General type of category of treatment
Ex: a doctor treats a patient with a skin condition with different creams to see which is most effective.
Levels
Amount or magnitude of the treatment
Ex: 5mg, 10mg, 15mg of a medication,

4. An experiment: Manipulates factor levels to create treatments
Randomly assigns subjects to these treatment levels
Compares the responses of the subject groups across treatment levels

5. An experiment:
Is prospective (identifies subjects in advance and collects data
as events unfold)
Differs from an observational study, which involves collecting
and analyzing data without changing existing conditions
(does not impose a treatment)
- retrospective (based on historical data)
Examples of observational study:
documenting amount of time taken to perform a task
observing food preferences of animals

6. Experimental Design Principles
The objective of experimental design is to conclude that differences in the dependent variable of an experiment are results of differences in the treatments being considered and can not be reasonably attributed to random error.

7. Three Principles of Design
Control:
Reduce variability by controlling sources of variation among experimental units- make conditions as similar as possible for all treatment groups
Reduces experimental bias and error
Only one experimental variable should be used
Randomization: subjects should be randomly divided into groups so each subject has the same chance of receiving treatment
Replication: the more subjects that are sampled according to each treatment, the more reliable the conclusions of the experimental design

8. Three Principles of Design
1. Control:
Good experimental design reduced variability by controlling the sources of variation
Comparison – must have at least two groups so effect of treatment can be compared with either the effect of a traditional treatment or the effect of no treatment
Make conditions as similar as possible for all treatment groups
Controlled variables/constants

9. Three Principles of Design
2. Randomization
Objects or individuals are randomly assigned (by chance) to an experimental group
Equalizes effects of unknown or uncontrollable sources of variation
Most reliable method of creating homogeneous treatment groups without involving any potential biases or judgments
Protects against variability/extraneous factors/chance
Reduces bias and experimental errors which can skew results

10. Randomized Design Types
Randomized Block Design
Used when an experimenter is aware of specific differences among groups of subjects or objects within an experimental group
Experimental subjects are first divided into homogeneous blocks before they are randomly assigned to a treatment group
Two parallel experiments
Reduces unwanted variation
Completely Randomized Design
Objects or subjects are assigned to groups completely at random
Number each subject and
use table of random numbers to assign subjects to groups

11. Randomized Design Types
Block Design Flow Chart Completely Randomized Design Flow Chart

12. Example of Randomized Block Design
A researcher is carrying out a study of the effectiveness of four different skin creams for the treatment of a certain skin disease.
He has eighty subjects and plans to divide them into 4 treatment groups of twenty subjects each.
Using a randomized block design, the subjects are assessed and put in blocks of four according to the severity of skin condition
four most severe cases are the first block
next four most severe cases are the second block and so on to the twentieth block
four members of each block are then randomly assigned, one to each of the four treatment groups

13. Example of Completely Randomized Design
A researcher is carrying out a study of the effectiveness of four different skin creams for the treatment of a certain skin disease.
He has eighty subjects.
He uses a computer to randomly assign 20 subjects into four different treatment groups.

14. Example 2 of Completely Randomized Design
Suppose we have 4 different diets which we want to compare. The diets are labeled Diet A, Diet B, Diet C, and Diet D. We are interested in how the diets affect the coagulation rates of rabbits. The coagulation rate is the time in seconds that it takes for a cut to stop bleeding. We have 16 rabbits available for the experiment, so we will use 4 on each diet.
Catch all the rabbits and label them 1-16.
Select four numbers 1-16 at random and put them in a cage to receive Diet A.
Then select another four numbers at random and put them in a cage to receive Diet B.
Continue until you have four cages with four rabbits each.
Each cage receives a different diet

15. Randomized Design Types
Matched Pairs Design
Special type of block design
Compares only two subjects or groups
Each subject gets both treatments and acts as their own control
Subjects paired in ways not under study
Reduces variability in subjects to insure single experimental variable
Ex: cola taste test
Each subject compares two colas and picks one
Order they taste colas is randomized
Students are tested to see if they learn better with music or quiet conditions

16. Example of Matched Pair Design
Reasercher tests to see if students concentrate better while listening to classical music or quiet room
Measure time it takes to complete puzzle in
each condition
Flip coin to determine which condition is used first (music or quiet)
If two subjects are used they must be matched as closely as possible on their attributes
Due to differences among students
Use same person to reduce variability
Differences can be attributed to only tested variable

17. Three Principles of Design
3. Replication
Independent repetition of the experiment: repeat entire experiment many times
Apply the treatments to a number of subjects (repeats)
Large sample size is essential
Outcome of an experiment on a single subject is an anecdote, not data
Reduces experimental error
Increases accuracy of population

18. Sample Size and Margin of Error

19. Diagrams of Experiments
It’s often helpful to diagram the procedure of an experiment
The following diagram emphasizes the random allocation of subjects to treatment groups, the separate treatments applied to these groups, and the ultimate comparison of results:

20. Components of Experiment: What are you testing?
What is a good question for an experiment?
One that is testable
Hypothesis:
Your best thinking about how the change you make might affect another factor
Tentative or trail solution to the question
Must be testable
If……IV………then ……DV……statement

21. Components of Experiment: Groups
Control Group
Normal condition/baseline
(business as usual)
Generally known outcome
Does not receive variable being tested for
Serves as standard of comparison to experimental group
Experimental Group
“Not normal” condition
Receives experimental variable being tested for
Outcome unknown

22. Components of Experiment: Variables
Variables: Conditions of the experiment
Independent Variable (IV): cause
Manipulated to assess its effect
May be administered to subjects by 'level
Ex: three levels of treatment
5mg, 10mg, 15mg of medication
Dependent Variable (DV): effect
Observed or measured
Controlled Variables/Constants: factors that remain same in both control and experimental groups
Experimental Variable
: factor being tested for
(always in the experimental group)

23. Components of Experiment: Data
Qualitative Observations:
Your perception of what happened during the experiment
Involves quality of results
Identifies trends in data
Do not contain numbers
Quantitative Observations:
Raw data in the form of numbers
Displayed in data tables and graphs

24. Components of Experiment: Data
Discrete Data
Categorical
Intervals between data have no meaning
Use bar graph
Continuous Data
Associated with measurements involving a standard scale
Measurements show a trend or relationship
Intervals between data have meaning
Use best fit or line graph

25. Components of Experiment: Data
Data Table: Setup

26. Components of Experiment: Data
Tables/Charts : One Variable Experiment
Effect of Time on Water Temperature
Effect of Fertilizer on Plant Growth

27. Components of Experiment: Data
Tables/Charts: Two Variable Experiment: Effects of a Hydrophilic Polymer and Irrigation Regime on Cactus Weight Gain

28. Components of Experiment: Data
Graphs: pictorial form of collected data Components of Graph

29. Constructing a Graph

30. Constructing a Graph

31. Constructing a Graph

32. Constructing a Graph

33. Constructing a Graph

34. Issues in Experimental Design: Bias
Experimental Bias
process where the scientists performing the research influence the results, in order to portray a certain outcome.
Blinding: method to avoid bias
Single blind:
Subjects do not know which treatment group they are assigned
Evaluators do not know which group subjects have been assigned
Double blind:
Everyone is blinded
Neither evaluators or subjects know treatment groups
Placebos: “fake” treatment that looks just like actual treatment
Best method to blind subjects from knowing if they are receiving treatment or not (sugar pill)

35. Issues in Experimental Design
Experimental Error
mistake in perception, measurement or process
Can be due to:
Design
Observation
Recording
Calculation
Measuring tool limitation
Types:
Type 1 error: false positive
Type 2 error: false negative

36. Analysis of Results Are the results statistically significant?
i.e., are the results due to the treatment or chance?
Null hypothesis (H0 ): no statistical observed effect of treatment
Results due to chance
Alternate Hypothesis (HA or H1): a statistical observed effect of treatment
Results due to experimental factor/treatment
Reject null hypothesis

37. Experimental Design Checklist
Define the objectives of the experiment (what are you testing for?)
What variable (IV/EV) is being used as treatment?
What experimental units/subjects will you use?
What are your control/experimental groups?
Identify and reduce all sources of variation including: treatment factors and their levels, variation within subject groups
What equipment will be used?
What will you be measuring as result (DV)?
Specify the measurements to be made.
Determine number of observations that need to be taken.
Determine timeline of experiment.