A control is a reference against which the results of an experimental manipulation can be compared. For example, if we wish to explore the effect of smoking on human lung tissue, we need a control of tissue samples from non-smokers to compare with our treatment group of tissue samples from smokers.
Example: Hypothesis: Giving a vitamin supplement to the feed of caged of rats leads to increased longevity.
We need a control group of rats that are identical in every way to our treatment group, except that they do not experience the experimental manipulation itself.
This is a negative control. A control group to which no manipulation is applied.
Example: Hypothesis: A particular novel treatment for common cold in humans produces better results that the currently used method.
We need a control group of rats that are identical in every way to our treatment group, except that they are given the old treatment.
This is a positive control.
Both can be used.
If we run our own control group, this is called a concurrent control. If instead we use historical data as a reference, this is called a historic control.
Example: Common cold example given before
A placebo or vehicle control is a treatment that is designed to appear exactly like the real treatment except for the parameter under investigation.
<![if !supportLists]>1) <![endif]>Completely Randomized Design
For a question such as “How does the regularity of feeding with a liquid fertilizer affects the growth of tomato plants?”
20 plants should be fed at 5 different rates. The total 100 plants should be assigned at random to the 5 feeding rates.
This experiment is called Completely Randomized Design because individuals are assigned to treatment groups completely at random.
A balanced experimental design has equal number of experimental units in each treatment group and an unbalanced design does not.
At the end of the experiment we look for differences between the groups of individuals.
The simplest design. We have varied a single factor experimentally (feeding rate) and then looked for differences between groups that experienced different levels of the factor.
Such a design is referred to as a one-factor design or a one-way design.
(Balanced, fully replicated, completely randomized one-factor design with five levels of the factor)
Method of analysis
If only two groups to analyze this can be done using t-test. If more that two groups then by using Analysis of variance (ANOVA)