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Do you think your boss will give you a raise for doing great work? If yes, then control variables are not important to you. This article is for those who want results but live in reality because it reveals the importance of control variables and provides examples that prove they matter. Control Variables are anything that can affect an experiment's outcome or result. From the scientist's perspective, control variables could be any uncontrolled aspect that may interfere with their study goals. With this information, scientists try to avoid them prior to conducting experiments so they can produce accurate data.
Unfortunately, since life is unpredictable, there are no guarantees that everything will go according to plan every time we do something. Life happens! For example, what if our supervisor does not agree to give us a raise after all? This is why control variables are important. Now let's see what are controlled variables.
There are two types of control variables: internal and external. Internal control variables are those that exist within the system being studied, whereas external control variables are those that originate from outside the system. Some examples of each are provided below.
The temperature of the room
The concentration of the acid in the beaker
The type of yeast used in the fermentation process
-The weather on the day of the experiment
-The traffic conditions on the day of the experiment
-The political environment during the time of the experiment
As you can see, control variables can be quite varied. The important thing is to be aware of them and try to account for as many as possible when designing an experiment. This will help produce accurate results and minimise the potential for confounding factors. Now that we have a better understanding of what control variables are let's look at some real-world examples.
-The type of soil used in a garden
-The amount of water given to a plant
-The variety of tomato plants grown
-The time of year the experiment is conducted
Each of these factors can potentially affect the outcome of an experiment studying tomatoes. By taking each of these variables into account, scientists can produce more accurate results. The type of soil used in a garden may affect the amount of water that is available to the plant, which could lead to differences in growth. The amount of water given to a plant may affect the number of nutrients that are available for consumption by other organisms, which can also influence results. The variety of tomato plants grown can impact whether or not it adapts successfully to its surrounding environment. The time of year an experiment is conducted could potentially change how much sunlight exists at different times throughout the day, which will certainly affect vegetation patterns.
If scientists were studying these factors with an aim to improve agricultural production, they would want control over each possible variable so they could produce accurate data and maximise potential yield. The same would apply to any kind of scientific study, including those associated with medicine.
This is the question posed to students during many introductory science courses! As you might have guessed, results would be unreliable and potentially useless. To prove this point, some examples show what happens when people do not consider control variables when conducting experiments.
An experiment studying the effects of caffeine on mice's stamina is conducted in a climate-controlled room at 20 degrees Celsius (68 Fahrenheit). Scientists only use male mice because they believe female mice process caffeine differently than males do. All mice receive one milligram of caffeine per kilogram of body weight 30 minutes before they are required to run on a treadmill. The mice that received caffeine performed significantly better than those that did not, and the scientists conclude that caffeine improves stamina in mice.
A second scientist wants to test to see if performance is affected by caffeine as well. This scientist conducts an experiment during an uncharacteristically hot day at 30 degrees Celsius (86 Fahrenheit). Mice receive one milligram of caffeine per kilogram of body weight 30 minutes before their anticipated workout time, but this time they take a jog on a treadmill for three hours. Results show no difference between the two groups, which leads the scientist to conclude that caffeine does not affect endurance in mice.
In both experiments, it is likely that many other factors also had an effect on the outcome, such as the fact that one group was confined to a small space while the other was running free. If these or any other uncontrolled variables had been accounted for, the results of the experiments might have been different.
Scientists would not be able to produce accurate and reliable results if they fail to take into account control variables.
Lack of consideration for what factors could affect the outcome of an experiment in how we get unusual results that don't show any significant difference. There are often many other factors present that can affect what happens during an experiment, but most scientists go about taking them one at a time so as not to muddy up their data pool. Sometimes, it becomes necessary to combine certain control variables to produce more reliable results with less testing. If this is the case, scientists may run all possible combinations of these factors before deciding on which factor(s) have the greatest impact.
Let's say there are two types of tomato seeds, one that must be grown in very bright sunlight and another that thrives in less light. A scientist wants to test which variety of tomato plants has a higher yield, so she sets up an experiment with both types of tomato seeds being grown side by side under varying levels of sunlight. However, to avoid variation between other variables, the scientist decides to conduct her experiment on greenhouses that are at the exact same temperature and humidity level. Unfortunately, testing all possible combinations of sun exposure for both seed varieties would take an extremely long time-it would require over 1 million trials! To reduce the amount of testing required, scientists often combine control variables into blocks. This means running each combination of factors only one time instead of every single trial.
For the tomato experiment, the scientist could test the yield of plants grown under different levels of sunlight, with each level of sunlight being combined with each variety of seed. This would require testing only 16 different combinations (2 levels of sunlight x 2 varieties of seed), which is much more manageable than 1 million. By combining control variables into blocks, scientists are able to produce more accurate results in a shorter amount of time.
Next time you're reading about the results of a scientific study, take a closer look at how the scientists accounted for potential confounding factors. If they didn't do a good job of controlling for variables, the results might not be very useful. However, if the scientists were thorough in their methods, the results are likely much more reliable. It is important to account for all potential factors that could affect the outcome when it comes to experiments. Failing to do so can lead to unreliable and potentially useless data. Scientists take this into account by using control variables-factors that are kept constant so that the results of an experiment are not skewed. By properly controlling for variables, scientists are able to produce more accurate and reliable data.
Controlling variables play an important role in experiments. If they didn't do a good job of controlling variables, the results might not be very useful. Tone warns against what could happen if certain precautions aren't taken. It discourages others from following their poor example. If you seek any further guidance with controlled variables, choose My Assignment Service! We are here to give you academic guidance in the form of live sessions so that you can gain advanced knowledge of the relevant topics and are able to produce an impressive assignment on your own! Fill out the form to connect with the
Albert Freddie is an educational technologist who has worked at the department of industry, innovation, and science, then joined a private institute to teach students about energy, and resources. He loves to write blogs and articles to help students in providing guidance in the subject area and assignments.
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