Designing Experiments: Definitions and Principles in Chapter 2

1. Chapter 2 Designing Experiments

2. Definitions: 1) Observational study - observe outcomes without imposing any treatment 2) Experiment - actively impose some treatment in order to observe the response

3. Experimental unit – the single individual (person, animal, plant, etc.) to which the different treatments are assigned Factor – is the explanatory variable – it’s what we test Level – a specific value for the factor

4. Response variable – what you measure Treatment – a specific experimental condition applied to the units

5. Control group – a group that is used to compare the factor against; can be a placebo or the “old” or current item Placebo – a “dummy” treatment that can have no physical effect

6. blinding - method used so that units do not know which treatment they are getting double blind - neither the units nor the evaluator know which treatment a subject received

7. Definition: Confounding variable – A variable whose effect on the response cannot be separated or distinguished from the effects of the factor on the response. - a variable that is related to both group membership and the response variable of interest in a research study Because observational studies may contain confounding variables, their results can NOT be used to show cause-effect relationships.

8. Principles of Experimental Design • Control the effects of extraneous variables on the response • Randomization – the use of chance to assign subjects to treatments • Replication of the experiment on many subjects to quantify the natural variation in the experiment

9. The ONLY way to show cause & effect is with a well-designed, well-controlled experiment!!!

10. Example 1: A farm-product manufacturer wants to determine if the yield of a crop is different when the soil is treated with three different types of fertilizers. Fifteen similar plots of land are planted with the same type of seed but are fertilized differently. At the end of the growing season, the mean yield from the sample plots is compared. Experimental units? Factors? Levels? Response variable? How many treatments? Plots of land Type of fertilizer Fertilizer types A & B Yield of crop 2

11. Example 3: A farm-product manufacturer wants to determine if the yield of a crop is different when the soil is treated with three different types of fertilizers. Fifteen similar plots of land are planted with the same type of seed but are fertilized differently. At the end of the growing season, the mean yield from the sample plots is compared. Why is the same type of seed used on all 15 plots? What are other potential extraneous variables? Does this experiment have a placebo? Explain It is part of the controls in the experiment. Type of soil, amount of water, etc. NO – a placebo is not needed in this experiment

12. Example 2: A consumer group wants to test cake pans to see which works the best (bakes evenly). It will test aluminum, glass, and plastic pans in both gas and electric ovens. Experiment units? Factors? Levels? Response variable? Number of treatments? Cake batter Two factors - type of pan & type of oven Type of pan has 3 levels (aluminum, glass, & plastic & type of oven has 2 levels (electric & gas) How evenly the cake bakes 6

13. Measure response for A Experimental Units Treatment B Treatment A Compare treatments Measure response for B Experimental Designs • Completely randomized design–experimental units are assigned at random to treatments Random Assignment

14. Experimental Units Compare the results from the 2 blocks Measure response for A Measure response for A Treatment B Treatment A Treatment B Treatment A Compare treatments for block 1 Compare treatments for block 2 Block 2 Block 1 Measure response for B Measure response for B Experimental Designs Continued . . . Units should be blocked on a variable that effects the response!!! 2. Randomized block –units are blocked into groups (homogeneous) and then randomly assigned to treatments Random Assignment Create blocks Random Assignment

15. Matched pairs - a special type of block design • match up experimental units according to similar characteristics & randomly assign on to one treatment & the other automatically gets the 2nd treatment • have each unit do both treatments in random order

16. Treatment B Treatment A Next, randomly assign one unit from a pair to Treatment A. The other unit gets Treatment B. Pair experimental units according to specific characteristics. This is one way to do a matched pairs design – another way is to have the individual unit do both treatments (as in a taste test or “before and after”).

17. Example 3: Suppose that the manufacturer wants to test a new fertilizer against the current one on the market. Ten 2-acre plots of land scattered throughout the county are used. Each plot is subdivided into two subplots, one of which is treated with the current fertilizer, and the other with the new fertilizer. Wheat is planted and the crop yields are measured. What type of design is this? Why use this method? When does randomization occur? Matched - pairs design Randomly assigned treatment to first acre of each two-acre plot

18. Example 5: Four new word-processing programs are to be compared by measuring the speed with which standard tasks can be completed. One hundred volunteers are randomly assigned to one of the four programs and their speeds are measured. Is this an experiment? Why or why not? Yes, a treatment is imposed. What type of design is this? Factors? Levels? Response variable? Completely randomized one factor: word-processing program with 4 levels speed

19. You could do a block design where each person uses each program in random order. Example 5: Four new word-processing programs are to be compared by measuring the speed with which standard tasks can be completed. One hundred volunteers are randomly designed to one of the four programs and their speeds are measured. Is there a potential confounding variable? Can this design be improved? Explain. NO, completely randomized designs have no confounding