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Experiment

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- An experiment deliberately imposes a treatment on a group of objects or subjects in the interest of observing the response.
- Differs from an observational study, which involves collecting and analyzing data without changing existing conditions

- The control group is practically identical to the treatment group, except for the single variable of interest whose effect is being tested, which is only applied to the treatment group.
- An example would be a drug trial.
- The group receiving the drug would be the treatment group and the one receiving the placebo would be the control group.

- In experiments, a treatment is something that researchers administer to experimental units .
- The treatment groups are the groups of subjects that received a particular treatment
- For example, in a drug test, three different groups of subjects received three different types of drugs
- The treatment is the administration of a particular drug type

- The proper organization of the experiment ensures that the right type of data, and enough of it, is available to answer the questions of interest as clearly and efficiently as possible.
- This process is called experimental design.
- Because the validity of a experiment is directly affected by its construction and execution, attention to experimental design is extremely important

- In an experimental design, a factor in an experiment is a controlled independent variable
- A variable whose levels are set by the experimenter

- A factor consists of categories of treatments
- Remember: Factors are independent variables

- From a statistical standpoint, the researcher looks for differences in the averages of the dependent variable(s) across the groups of independent variables

- When researchers fail to control for the effects of the differences in subjects, it can lead to experimental bias
- Experimental bias is the favoring of certain outcomes over others

- Because it is generally extremely difficult for experimenters to eliminate bias using only their expert judgment, the use of randomization in experiments is common practice.
- In a randomized experimental design, objects or individuals are randomly assigned (by chance) to an experimental group.

- To improve the significance of an experimental result, replication, the repetition of an experiment on a large group of subjects, is required.
- If a treatment is truly effective, the long-term averaging effect of replication will reflect its experimental worth.
- If it is not effective, then the few members of the experimental population who may have reacted to the treatment will be negated by the large numbers of subjects who were unaffected by it.

- Experimental Designs are defined by their formats
- Examples of these formats include:
- One-way Analysis of Variance
- Multivariate Analysis of Variance
- Factorial Analysis of Variance
- Split Plot Design
- Latin Square Design

- A One-way Analysis of Variance identifies significant differences between group averages
- In a One-way Analysis of Variance, the researcher randomly selects subjects and assigns them to one of three different forklift driving training programs.
- The three different programs are:
- Classroom based only
- Hands-on only
- Combination hands-on and classroom

- Our treatment variable (or factor) is “forklift training program” and it has three levels (listed above)

- The researcher randomly selects 10 subjects for each of the different training program, formats
- If the subjects are not randomly selected, what could occur?

- The researcher will compare the average number of forklift accidents incurred by each group to determine if there is a significant difference between the averages

- The ANOVA utilizes the F-ratio to determine if there is a significant difference between the group averages
- The null hypothesis is:
- Ave Grp 1 = Ave Grp 2 = Ave Grp 3

- The alternative hypothesis is:
- AveGrp 1 NE Ave Grp 2 NE Ave Grp 3

- A significant F-ratio indicates there is a significant difference between the group averages

- The ANOVA procedure found the significance of the F-ratio to be .013.
- If an Alpha level of .05 is used, then because .013 is less than .05, one can conclude there is a significant difference between the group averages
- The odds of these results occurring totally due to random chance is .013.
- Another way of saying this is “The researcher has a .013 percent chance of rejecting the Null Hypothesis when the Null Hypothesis is in fact true”

- When the ANOVA test result is found to be significant, the next step is to run a post-hoc test to determine where the significance lies between groups.
- There are a number of different post-hoc tests that can be run (Scheffe’s, Tukey’s, etc.)
- For example, is Group 1 significantly different from Groups 2 or 3
- Is Group 2 significantly different from Groups 1 or 3
- Etc.