- Identify the important characteristics of an experiment.
- Distinguish between confounding and lurking variables.
- Use a random number generator to randomly assign experimental units to treatment groups.
- Identify experimental situations in which blocking is necessary or appropriate and create a blocking scheme for such experiments.
- Identify experimental situations in which a matched pairs design is necessary or appropriate and explain how such a design could be implemented.
- Identify the reasons for and the advantages of blind experiments.
- Distinguish between correlation and causation.
Confounding and Lurking Variables
In an observational study such as the Royal Society’s connecting gender and a mother’s diet, it is possible that there is a third variable that was not observed that is causing a change in both the explanatory and response variables. A variable that is not included in a study but that may still have an effect on the other variables involved is called a lurking variable. Perhaps the existence of this variable is unknown or its effect is not suspected.
Example: It's possible that in the study presented above, the mother’s exercise habits caused both her increased consumption of calories and her increased likelihood of having a male child.
A slightly different type of additional variable is called a confounding variable. Confounding variables are those that affect the response variable and are also related to the explanatory variable. The effect of a confounding variable on the response variable cannot be separated from the effect of the explanatory variable. They are both observed, but it cannot be distinguished which one is actually causing the change in the response variable.
Example: The study described above also mentions that the habit of skipping breakfast could possibly depress glucose levels and lead to a decreased chance of sustaining a viable male embryo. In an observational study, it is impossible to determine if it is nutritional habits in general, or the act of skipping breakfast, that causes a change in gender birth rates. A well-designed statistical experiment has the potential to isolate the effects of these intertwined variables, but there is still no guarantee that we will ever be able to determine if one of these variables, or some other factor, causes a change in gender birth rates.
Experiments: Treatments, Randomization, and Replication
There are three elements that are essential to any statistical experiment that can earn the title of a randomized clinical trial. The first is that a treatment must be imposed on the subjects of the experiment. In the example of the British study on gender, we would have to prescribe different diets to different women who were attempting to become pregnant, rather than simply observing or having them record the details of their diets during this time, as was done for the study. The next element is that the treatments imposed must be randomly assigned. Random assignment helps to eliminate other confounding variables. Just as randomization helps to create a representative sample in a survey, if we randomly assign treatments to the subjects, we can increase the likelihood that the treatment groups are equally representative of the population. The other essential element of an experiment is replication. The conditions of a well-designed experiment will be able to be replicated by other researchers so that the results can be independently confirmed.
To design an experiment similar to the British study, we would need to use valid sampling techniques to select a representative sample of women who were attempting to conceive. (This might be difficult to accomplish!) The women might then be randomly assigned to one of three groups in which their diets would be strictly controlled. The first group would be required to skip breakfast, the second group would be put on a high-calorie, nutrition-rich diet, and the third group would be put on a low-calorie, low-nutrition diet. This brings up some ethical concerns. An experiment that imposes a treatment which could cause direct harm to the subjects is morally objectionable, and should be avoided. Since skipping breakfast could actually harm the development of the child, it should not be part of an experiment.
It would be important to closely monitor the women for successful conception to be sure that once a viable embryo is established, the mother returns to a properly nutritious pre-natal diet. The gender of the child would eventually be determined, and the results between the three groups would be compared for differences.
Let’s say that your statistics teacher read somewhere that classical music has a positive effect on learning. To impose a treatment in this scenario, she decides to have students listen to an MP3 player very softly playing Mozart string quartets while they sleep for a week prior to administering a unit test. To help minimize the possibility that some other unknown factor might influence student performance on the test, she randomly assigns the class into two groups of students. One group will listen to the music, and the other group will not. When the treatment of interest is actually withheld from one of the treatment groups, it is usually referred to as the control group. By randomly assigning subjects to these two groups, we can help improve the chances that each group is representative of the class as a whole.
Placebos and Blind Experiments
In medical studies, the treatment group usually receives some experimental medication or treatment that has the potential to offer a new cure or improvement for some medical condition. This would mean that the control group would not receive the treatment or medication. Many studies and experiments have shown that the expectations of participants can influence the outcomes. This is especially true in clinical medication studies in which participants who believe they are receiving a potentially promising new treatment tend to improve. To help minimize these expectations, researchers usually will not tell participants in a medical study if they are receiving a new treatment. In order to help isolate the effects of personal expectations, the control group is typically given a placebo. The placebo group would think they are receiving the new medication, but they would, in fact, be given medication with no active ingredient in it. Because neither group would know if they are receiving the treatment or the placebo, any change that might result from the expectation of treatment (this is called the placebo effect) should theoretically occur equally in both groups, provided they are randomly assigned. When the subjects in an experiment do not know which treatment they are receiving, it is called a blind experiment.
Example: If you wanted to do an experiment to see if people preferred a brand-name bottled water to a generic brand, you would most likely need to conceal the identity of the type of water. A participant might expect the brand-name water to taste better than a generic brand, which would alter the results. Also, sometimes the expectations or prejudices of the researchers conducting the study could affect their ability to objectively report the results, or could cause them to unknowingly give clues to the subjects that would affect the results. To avoid this problem, it is possible to design the experiment so that the researcher also does not know which individuals have been given the treatment or placebo. This is called a double-blind experiment. Because drug trials are often conducted or funded by companies that have a financial interest in the success of the drug, in an effort to avoid any appearance of influencing the results, double-blind experiments are considered the gold standard of medical research.
Blocking in an experiment serves a purpose similar to that of stratification in a survey. For example, if we believe men and women might have different opinions about an issue, we must be sure those opinions are properly represented in the sample. The terminology comes from agriculture. In testing different yields for different varieties of crops, researchers would need to plant crops in large fields, or blocks, that could contain variations in conditions, such as soil quality, sunlight exposure, and drainage. It is even possible that a crop’s position within a block could affect its yield. Similarly, if there is a sub-group in the population that might respond differently to an imposed treatment, our results could be confounded. Let’s say we want to study the effects of listening to classical music on student success in statistics class. It is possible that boys and girls respond differently to the treatment, so if we were to design an experiment to investigate the effect of listening to classical music, we want to be sure that boys and girls were assigned equally to the treatment (listening to classical music) and the control group (not listening to classical music). This procedure would be referred to as blocking on gender. In this manner, any differences that may occur in boys and girls would occur equally under both conditions, and we would be more likely to be able to conclude that differences in student performance were due to the imposed treatment. In blocking, you should attempt to create blocks that are homogenous (the same) for the trait on which you are blocking.
Example: In your garden, you would like to know which of two varieties of tomato plants will have the best yield. There is room in your garden to plant four plants, two of each variety. Because the sun is coming predominately from one direction, it is possible that plants closer to the sun would perform better and shade the other plants. Therefore, it would be a good idea to block on sun exposure by creating two blocks, one sunny and one not.
You would randomly assign one plant from each variety to each block. Then, within each block, you would randomly assign each variety to one of the two positions.
This type of design is called randomized block design.
Matched Pairs Design
A matched pairs design is a type of randomized block design in which there are two treatments to apply.
Example: Suppose you were interested in the effectiveness of two different types of running shoes. You might search for volunteers among regular runners using the database of registered participants in a local distance run. After personal interviews, a sample of 50 runners who run a similar distance and pace (average speed) on roadways on a regular basis could be chosen. Suppose that because you feel that the weight of the runners will directly affect the life of the shoe, you decided to block on weight. In a matched pairs design, you could list the weights of all 50 runners in order and then create 25 matched pairs by grouping the weights two at a time. One runner would be randomly assigned shoe A, and the other would be given shoe B. After a sufficient length of time, the amount of wear on the shoes could be compared.
In the previous example, there may be some potential confounding influences. Factors such as running style, foot shape, height, or gender may also cause shoes to wear out too quickly or more slowly. It would be more effective to compare the wear of each shoe on each runner. This is a special type of matched pairs design in which each experimental unit becomes its own matched pair. Because the matched pair is in fact two different observations of the same subject, it is called a repeated measures design. Each runner would use shoe A and shoe B for equal periods of time, and then the wear of the shoes for each individual would be compared. Randomization could still be important, though. Let’s say that we have each runner use each shoe type for a period of 3 months. It is possible that the weather during those three months could influence the amount of wear on the shoe. To minimize this, we could randomly assign half the subjects shoe A, with the other half receiving shoe B, and then switch after the first 3 months.
The important elements of a statistical experiment are randomness, imposed treatments, and replication. The use of these elements is the only effective method for establishing meaningful cause-and-effect relationships. An experiment attempts to isolate, or control, other potential variables that may contribute to changes in the response variable. If these other variables are known quantities but are difficult, or impossible, to distinguish from the other explanatory variables, they are called confounding variables. If there is an additional explanatory variable affecting the response variable that was not considered in an experiment, it is called a lurking variable. A treatment is the term used to refer to a condition imposed on the subjects in an experiment. An experiment will have at least two treatments. When trying to test the effectiveness of a particular treatment, it is often effective to withhold applying that treatment to a group of randomly chosen subjects. This is called a control group. If the subjects are aware of the conditions of their treatment, they may have preconceived expectations that could affect the outcome. Especially in medical experiments, the psychological effect of believing you are receiving a potentially effective treatment can lead to different results. This phenomenon is called the placebo effect. When the participants in a clinical trial are led to believe they are receiving the new treatment, when, in fact, they are not, they receive what is called a placebo. If the participants are not aware of the treatment they are receiving, it is called a blind experiment, and when neither the participant nor the researcher is aware of which subjects are receiving the treatment and which subjects are receiving a placebo, it is called a double-blind experiment.
Blocking is a technique used to control the potential confounding of variables. It is similar to the idea of stratification in sampling. In a randomized block design, the researcher creates blocks of subjects that exhibit similar traits that might cause different responses to the treatment and then randomly assigns the different treatments within each block. A matched pairs design is a special type of design where there are two treatments. The researcher creates blocks of size 2 on some similar characteristic and then randomly assigns one subject from each pair to each treatment. Repeated measures designs are a special matched pairs experiment in which each subject becomes its own matched pair by applying both treatments to the subject and then comparing the results.
Points to Consider
- What are some other ways that researchers design more complicated experiments?
- When one treatment seems to result in a notable difference, how do we know if that difference is statistically significant?
- How can the selection of samples for an experiment affect the validity of the conclusions?
- As part of an effort to study the effect of intelligence on survival mechanisms, scientists recently compared a group of fruit flies intentionally bred for intelligence to the same species of ordinary flies. When released together in an environment with high competition for food, the percentage of ordinary flies that survived was significantly higher than the percentage of intelligent flies that survived.
- Identify the population of interest and the treatments.
- Based on the information given in this problem, is this an observational study or an experiment?
- Based on the information given in this problem, can you conclude definitively that intelligence decreases survival among animals?
- In order to find out which brand of cola students in your school prefer, you set up an experiment where each person will taste two brands of cola, and you will record their preference.
- How would you characterize the design of this study?
- If you poured each student a small cup from the original bottles, what threat might that pose to your results? Explain what you would do to avoid this problem, and identify the statistical term for your solution.
- Let’s say that one of the two colas leaves a bitter after-taste. What threat might this pose to your results? Explain how you could use randomness to solve this problem.
- You would like to know if the color of the ink used for a difficult math test affects the stress level of the test taker. The response variable you will use to measure stress is pulse rate. Half the students will be given a test with black ink, and the other half will be given the same test with red ink. Students will be told that this test will have a major impact on their grades in the class. At a point during the test, you will ask the students to stop for a moment and measure their pulse rates. In preparation for this experiment, you measure the at-rest pulse rates of all the students in your class.
Here are those pulse rates in beats per minute:
At Rest Pulse Rate
(a) Using a matched pairs design, identify the students (by number) that you would place in each pair.
(b) Seed the random number generator on your calculator using 623.
Use your calculator to randomly assign each student to a treatment. Explain how you made your assignments.
(a) Identify any potential lurking variables in this experiment.
(b) Explain how you could redesign this experiment as a repeated measures design?
What is the population in this example?
One group began the diet immediately; another group waited three months to start it. In the first group, 38% of the children experienced a 50% reduction in seizure rates, and in the second group, only 6 percent saw a similar reduction. What information would you need to be able to conclude that this was a valid experiment?
(a) Identify the treatment and control groups in this experiment.
(b) What conclusion could you make from the reported results of this experiment?
- Researchers want to know how chemically fertilized and treated grass compares to grass grown using only organic fertilizer. Also, they believe that the height at which the grass is cut will affect the growth of the lawn. To test this, grass will be cut at three different heights: 1 inch, 2 inches, and 4 inches. A lawn area of existing healthy grass will be divided up into plots for the experiment. Assume that the soil, sun, and drainage for the test areas are uniform. Explain how you would implement a randomized block design to test the different effects of fertilizer and grass height. Draw a diagram that shows the plots and the assigned treatments.