Chapter 2 Experimental Design Definitions 1 Observational study

Chapter 2 Experimental Design

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

I’ve developed a new rabbit food, Hippity Hop. Makes fur soft & shiny! Rabbit Food Increases energy! 100% of daily vitamins & essential oils!

Can I just make these claims? NO What must I do to make these claims? Do an experiment Who (what) should I test this on? Rabbits What do I test? The type of food

3)Experimental unit – the single individual (person, animal, plant, etc. ) to which the different treatments are assigned 4) Factor – is the explanatory variable 5) Level – a specific value for the factor

6) Response variable – what you measure 7) Treatment – a specific experimental condition applied to the units

I plan to test my new rabbit food. What are my experimental units? Rabbits What is my factor? Type of food What is the response variable? How well they grow

I’ll use my pet rabbit, Lucky! Hippity Hop Since Lucky’s coat is shinier & he has more energy, then Hippity Hop is a better rabbit food!

8) Control group – a group that is used to compare the factor against; can be a placebo or the “old” or current item 9) Placebo – a “dummy” treatment that can have no physical effect

Old Food Hippity Hop Now I’ll use Lucky & my friend’s rabbit, Flash. Lucky gets Hippity Hop food & Flash gets the old rabbit food. WOW! Lucky is bigger & shinier so Hippity Hop is better!

Old Food Hippity Hop The first five rabbits that I catch will get Hippity Hop food and the remaining five will get the old food. The Hippity Hop rabbits have scored higher so it’s the better food!

Old Food 5 73 98 Hippity Hop Number from – Placethe therabbits numbers in a 1 hat. The first five numbers 10. The remaining rabbitspulled get from the be the hat old will food. rabbits that get Hippity Hop food. 6 2 4 the rabbits & found I evaluated 5 9 10 that 1 the 3 rabbits eating 7 Hippity Hop 8 are better than the old food!

10) blinding - method used so that units do not know which treatment they are getting 11) double blind - neither the units nor the evaluator know which treatment a subject received

Rabbit Food Hippity Hop Rabbit Food makes fur soft and shiny, & increases energy for ALL types of rabbits! Can I make this claim?

Principles of Experimental Design • Control of effects of extraneous variables on the response – by comparing treatment groups to a control group (placebo or “old”) • Replication of the experiment on many subjects to quantify the natural variation in the experiment • Randomization – the use of chance to assign subjects to treatments

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

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? Plots of land Factors? Type of fertilizer Levels? Fertilizer types A, B, & C Response variable? Yield of crop How many treatments? 3

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? Cake Factors? Levels? 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) Response variable? How evenly the cake bakes Number of treatments? 6

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? It is part of the controls in the experiment. What are other potential extraneous variables? Type of soil, amount of water, etc. Does this experiment have a placebo? Explain NO – a placebo is not needed in this experiment

Experiment Designs Random assignment • Completely randomized – all experimental units are allocated at random among all treatments

Treatment A Treatment B Treatment C Treatment D Randomly assign experimental units to treatments Completely randomized design

Random assignment • Randomized block – units are blocked into groups (homogeneous) and then randomly assigned to treatments Units should be blocked on a variable that effects the response!!!

Treatment A Treatment B Put into homogeneous Randomly assign groups experimental units to treatments Randomized block design

• 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 2 nd treatment – have each unit do both treatments in random order – the assignment of treatments is dependent

Treatment A Treatment B experimental Next, Pair randomly assign units according to one unit from a pair to specific Treatment A. The characteristics. other unit gets Treatment B. 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).

12) Confounding variable – the effect of the confounding variable on the response cannot be separated from the effects of the explanatory variable (factor)

Suppose we wish to test a new deodorant against one currently on the market.

Treatment A Treatment B One group is assigned to Treatment B treatment A & the other group to treatment B. Confounding does NOT occur in a completely randomized Treatment & groupdesign! are confounded

Example 4: An article from USA Today reports the number of victims of violent crimes per 1000 people. 51 victims have never been married, 42 are divorced or separated, 13 are married, and 8 are widowed. Is this an experiment? Why or why not? No, no treatment was imposed on people. What is a potential confounding Age – younger people are more at risk variable? to be victims of violent crimes

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? Completely randomized Factors? Levels? one factor: word-processing program with 4 levels Response variable? speed

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 do the a block designed. You to could one of four programs design where each person and theiruses speeds are measured. each program in random order. Is there a potential confounding variable? Can this design NO, completely randomized designs have no confounding be improved? Explain.

Example 6: 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

Randomization reduces bias by spreading any uncontrolled Is there another way confounding variables evenly to reduce variability? throughout the treatment groups. Blocking also helps reduce variability. Bias is a systematic error in measuring the by estimate Variability is controlled sample size. Larger samples produce statistics with less variability.

High bias & high variability Low bias & high variability High bias & low variability Low bias & low variability