A controlled experiment is one in which everything remains constant except for one variable. Typically, a dataset is taken as a control group, which is usually the normal or usual state, and one or more groups are examined in which all conditions are identical to the control group and to each other, except for one variable.
Sometimes it is necessary to change more than one variable, but all other experimental conditions are controlled so that only the variables being examined change. And what is measured is the number of variables or the way they change.
Biologists and other scientists use the scientific method to ask questions about the natural world. The scientific method begins with an observation, which leads the scientist to ask a question. Next, he comes up with a hypothesis, a testable explanation that answers the question.
According to Pronzato (2008), a hypothesis is not necessarily correct. This is an “assumption” and the scientist must test it to see if it is correct. Scientists test hypotheses by making predictions: if the \text XXstart text, X, end text hypothesis is correct, then \text YYstart text, Y, end text should be true. They then conduct experiments or observations to check if the predictions are correct. If they are, the hypothesis is supported. If they are not, it may be time to raise a new hypothesis.
Controlled experiment example
Suppose you want to know if the type of soil affects the time it takes for a seed to germinate and decide to organize a controlled experiment to answer the question. You could take five identical pots, fill each of them with a different type of soil, plant identical bean seeds in each pot, place the pots in a sunny window, water them equally, and measure the time it takes for each pot’s seeds to sprout.
This is a controlled experiment because your goal is to keep all the variables constant, except the type of land you use. You control these characteristics.
Why Controlled Experiments Are Important
The great advantage of a controlled experiment is that you can eliminate much of the uncertainty about your results. If you couldn’t control each variable, you could end up with a confusing result.
For example, if you planted different types of seeds in each of the pots, trying to determine if the type of soil affects germination, you might find that some types of seeds germinate faster than others. I could not say, with any degree of certainty, that the germination rate is due to the type of soil. It could also be due to the type of seeds.
Or, if you had placed some pots in a sunny window and others in the shade or watered some pots more than others, you could get mixed results. The value of a controlled experiment is that it provides a high degree of confidence in the result. You know which variable caused a change or not.
Are all experiments controlled?
No, they are not. An example of an area where controlled experiments are difficult is that of human testing. Let’s say you want to know if a new diet pill helps you lose weight. You can gather a sample of people, give each of them the pill, and measure their weight. You can try to control for all possible variables, such as how much exercise they do or the calories they consume.
However, you will have several uncontrolled variables, which may include age, sex, genetic predisposition to a high or low metabolism, how overweight they were before starting the test, whether they inadvertently eat something that interacts with the drug, etc.
Scientists try to record as much data as possible when conducting uncontrolled experiments, so they can see additional factors that may be affecting their results. Although it is more difficult to draw conclusions from uncontrolled experiments, new patterns often emerge that would not have been observable in a controlled experiment.
For example, you may notice that the dietary drug seems to work in female subjects, but not in male subjects, and this can lead to further experimentation and possible advancement. If you had only been able to perform a controlled experiment, perhaps only with male clones, you would have overlooked this connection.
How are hypotheses tested?
When possible, scientists test their hypotheses using controlled experiments. A controlled experiment is a scientific test performed under controlled conditions, meaning that only one (or a few) factors are modified at a time, while all the others remain constant.
In some cases, there is no good way to test a hypothesis using a controlled experiment (for practical or ethical reasons). In that case, a scientist can test a hypothesis by making predictions about patterns that should be seen in nature if the hypothesis is correct. You can then collect data to see if the pattern is actually present.
Control and experimental groups
There are two groups in the experiment, and they are identical except that one receives a treatment (water) while the other does not. The group that receives the treatment in an experiment (here, the watered pot) is called the experimental group, while the group that does not receive the treatment (here, the dry pot) is called the control group. The control group provides a baseline that allows us to see if the treatment has an effect.
Independent and dependent variables
The factor that is different between the control and experimental groups (in this case, the amount of water) is known as the independent variable. This variable is independent because it does not depend on what happens in the experiment. Instead, it is something that the experimenter applies or chooses himself.
The dependent variable in an experiment, according to Box et al (2005), is the response that is measured to see if the treatment has had an effect. In this case, the fraction of bean seeds that sprouted is the dependent variable. This dependent variable (fraction of seeds sprouting) depends on the independent variable (the amount of water), and not the other way around.
Experimental data (singular: datum) are the observations made during the experiment. In this case, the data we collected was the number of bean sprouts in each pot after a week.
Variability and repetition
Of the ten bean seeds watered, only nine came out. What happened to the tenth seed? That seed may have been dead, unhealthy, or simply slow to sprout. Especially in biology (which studies complex living things), there are often variations in the material used for an experiment — in this case, bean seeds — that the experimenter cannot see.
Because of this potential for variation, biology experiments should have a large sample size and, ideally, be repeated several times. Sample size refers to the number of individual items tested in an experiment, in this case, 101010 bean seeds per group. Having more samples and repeating the experiment more times makes it less likely that we will come to an erroneous conclusion due to random variation.
Biologists and other scientists according to Cresswell (2008), also use statistical tests to help them distinguish real differences from those due to random variation (for example, when comparing experimental and control groups).
Controlled Experiment Case Study
As a more realistic example of a controlled experiment, let’s examine a recent study on coral bleaching. Corals usually have tiny photosynthetic organisms living inside, and bleaching occurs when they leave the coral, usually due to environmental stress.
Much research on the cause of bleaching has focused on water temperature
However, a team of Australian researchers hypothesized that other factors could also be important. Specifically, they tested the hypothesis that if ocean waters are more acidic, they could also favor bleaching.
What kind of experiment would you do to test this hypothesis? Think about it:
What would be your control and experimental groups, what would be their independent and dependent variables and what outcomes you would predict in each group
Testing of non-experimental hypotheses
Some types of hypotheses cannot be tested in controlled experiments for ethical or practical reasons. For example, a hypothesis about a viral infection cannot be proven by dividing healthy people into two groups and infecting one of them: infecting healthy people would not be safe or ethical. Similarly, an ecologist studying the effects of rain cannot make it rain in one part of a continent, while keeping another part dry as control.
In situations like these, biologists can use non-experimental ways of hypothesis testing. It then collects and analyzes the data to see if the patterns are actually present.
Case Study: Coral Bleaching and Temperature
A good example of observational hypothesis testing comes from early studies on coral bleaching. Bleaching occurs when corals lose the photosynthetic microorganisms that live inside, causing them to turn white. The researchers suspected that high water temperature could cause bleaching, and tested this hypothesis experimentally on a small scale (using coral fragments isolated in tanks).
However, what ecologists most wanted to know was whether the water temperature caused the bleaching of many coral species in their natural environment. This broader question could not be answered experimentally, as it would not be ethical (or even possible) to artificially change the temperature of the water surrounding entire coral reefs.
To test the hypothesis that natural bleaching events were caused by rising water temperatures, a team of researchers wrote a computer program to predict bleaching events based on real-time water temperature data. For example, this program would generally predict the bleaching of a particular reef when the water temperature in the reef area exceeded its average monthly maximum by 111.
The computer program was able to predict many bleaching episodes weeks or even months before they occurred, including a major bleaching episode on the Great Barrier Reef in 1998.
The fact that a temperature-based model could predict bleaching episodes supported the hypothesis that high water temperature causes bleaching in natural coral reefs.
Musical preference in dogs
Do dogs have a musical taste? He may have considered it, and so has science. Believe it or not, researchers have tested dogs’ reactions to various musical genres. To organize a controlled experiment like this, the scientists had to take into account the numerous variables that affect each dog during the tests. The environment the dog is in when listening to music, the volume of music, the presence of humans, and even temperature were variables the researchers had to consider.
In this case, the musical genre was the independent variable. In other words, to see if dogs change their behavior in response to different types of music, a controlled experiment had to limit the interaction of the other variables on dogs. Normally, such an experiment is carried out in the same place, with the same lighting, furniture and conditions each time. This ensures that dogs do not change their behavior in response to the room. To make sure that dogs don’t react to humans or just the noise of music, there can be no one else in the room and the music must be played at the same volume for each genre. Scientists will develop protocols for their experiment, which will ensure control of many other variables.
Another way to perform the experiment
This experiment could also divide the dogs into two groups, testing the music in only one of them. The control group would be used to establish baseline behavior and see how dogs behave without music. Next, the other group could be observed and the differences in the group’s behavior analyzed. By rating behaviors on a quantitative scale, statistics can be used to analyze the behavior difference, and see if it was large enough to be considered meaningful. This basic experiment was carried out with a large number of dogs, analyzing their behavior with a variety of different musical genres. Dogs were found to display more relaxed and calm behaviors when a specific type of music is played. It was discovered that dogs are the ones who enjoy reggae the most.
Scurvy in sailors
In the early 1700s, the world was a rapidly expanding place. Ships were built and shipped all over the world, carrying thousands and thousands of sailors. Most of these sailors were fed the cheapest possible diets, not only because they lowered the costs of goods, but also because fresh food is very difficult to maintain at sea. Today we know that the lack of essential vitamins and nutrients can cause serious deficiencies that manifest as diseases. One of these diseases is scurvy.
Scurvy is caused by a simple vitamin C deficiency, but its effects can be brutal. Although early symptoms only include a general feeling of weakness, the continued lack of vitamin C will cause blood cells and blood-carrying vessels to rupture. The result is a leakage of blood from the vessels. Eventually, people bleed internally and die. Before controlled experiments were commonplace, a simple doctor decided to tackle the problem of scurvy. James Lind of the Royal Navy devised a simple controlled experiment to find the best cure for scurvy.
How the research was conducted
He separated sailors with scurvy into several groups. He subjected them to the same controlled condition and gave them the same diet except for one element. Each group underwent a different treatment or remedy, taken with their food. Some of these remedies included barley water, cider, and a regiment of oranges and lemons. This created the first clinical trial, or test of the efficacy of certain treatments in a controlled experiment. Lind discovered that oranges and lemons helped sailors recover quickly, and within a few years the Royal Navy had developed protocols for growing small leafy greens containing high amounts of vitamin C to feed their sailors.
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You might also be interested in: Univariate Analysis
Box, George E. P., et al. Statistics for Experimenters: Design, Innovation, and Discovery. Wiley-Interscience, to John Wiley & Soncs, Inc., Publication, 2005.
Creswell, John W. Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research. Pearson/Merrill Prentice Hall, 2008.
Pronzato, L. “Optimal experimental design and some related control problems”. Automatic. 2008.