The word control in science

A controlled experiment is a scientific test done under controlled conditions meaning that just one (or a few) factors are changed at a time while all others are kept constant.

See also what effect does succession have on the biodiversity of ecosystems

What is a word for control?

Some common synonyms of control are authority command dominion jurisdiction power and sway.

What is the function of a control in an experiment?

Controls allow the experimenter to minimize the effects of factors other than the one being tested. It’s how we know an experiment is testing the thing it claims to be testing. This goes beyond science — controls are necessary for any sort of experimental testing no matter the subject area.

What it means to have control?

the act or power of controlling regulation domination or command: Who’s in control here? the situation of being under the regulation domination or command of another: The car is out of control. check or restraint: Her anger is under control.

What is a control in research?

Control refers to the effort by the researcher to remove the influence of any extraneous confounding variable on the DV under investigation. To help illustrate the nature of control consider the following example experiment.

What’s the meaning of control variable?

A control variable is anything that is held constant or limited in a research study. It’s a variable that is not of interest to the study’s aims but is controlled because it could influence the outcomes.

What is a controlled variable in Science example?

From Wikipedia, the free encyclopedia

A scientific control is an experiment or observation designed to minimize the effects of variables other than the independent variable (i.e. confounding variables).^[1] This increases the reliability of the results, often through a comparison between control measurements and the other measurements. Scientific controls are a part of the scientific method.

Controlled experiments[edit]

Controls eliminate alternate explanations of experimental results, especially experimental errors and experimenter bias. Many controls are specific to the type of experiment being performed, as in the molecular markers used in SDS-PAGE experiments, and may simply have the purpose of ensuring that the equipment is working properly. The selection and use of proper controls to ensure that experimental results are valid (for example, absence of confounding variables) can be very difficult. Control measurements may also be used for other purposes: for example, a measurement of a microphone’s background noise in the absence of a signal allows the noise to be subtracted from later measurements of the signal, thus producing a processed signal of higher quality.

For example, if a researcher feeds an experimental artificial sweetener to sixty laboratories rats and observes that ten of them subsequently become sick, the underlying cause could be the sweetener itself or something unrelated. Other variables, which may not be readily obvious, may interfere with the experimental design. For instance, the artificial sweetener might be mixed with a dilutant and it might be the dilutant that causes the effect. To control for the effect of the dilutant, the same test is run twice; once with the artificial sweetener in the dilutant, and another done exactly the same way but using the dilutant alone. Now the experiment is controlled for the dilutant and the experimenter can distinguish between sweetener, dilutant, and non-treatment. Controls are most often necessary where a confounding factor cannot easily be separated from the primary treatments. For example, it may be necessary to use a tractor to spread fertilizer where there is no other practicable way to spread fertilizer. The simplest solution is to have a treatment where a tractor is driven over plots without spreading fertilizer and in that way, the effects of tractor traffic are controlled.

The simplest types of control are negative and positive controls, and both are found in many different types of experiments.^[2] These two controls, when both are successful, are usually sufficient to eliminate most potential confounding variables: it means that the experiment produces a negative result when a negative result is expected, and a positive result when a positive result is expected.

Negative[edit]

Where there are only two possible outcomes, e.g. positive or negative, if the treatment group and the negative control both produce a negative result, it can be inferred that the treatment had no effect. If the treatment group and the negative control both produce a positive result, it can be inferred that a confounding variable is involved in the phenomenon under study, and the positive results are not solely due to the treatment.

In other examples, outcomes might be measured as lengths, times, percentages, and so forth. In the drug testing example, we could measure the percentage of patients cured. In this case, the treatment is inferred to have no effect when the treatment group and the negative control produce the same results. Some improvement is expected in the placebo group due to the placebo effect, and this result sets the baseline upon which the treatment must improve upon. Even if the treatment group shows improvement, it needs to be compared to the placebo group. If the groups show the same effect, then the treatment was not responsible for the improvement (because the same number of patients were cured in the absence of the treatment). The treatment is only effective if the treatment group shows more improvement than the placebo group.

Positive[edit]

Positive controls are often used to assess test validity. For example, to assess a new test’s ability to detect a disease (its sensitivity), then we can compare it against a different test that is already known to work. The well-established test is a positive control since we already know that the answer to the question (whether the test works) is yes.

Similarly, in an enzyme assay to measure the amount of an enzyme in a set of extracts, a positive control would be an assay containing a known quantity of the purified enzyme (while a negative control would contain no enzyme). The positive control should give a large amount of enzyme activity, while the negative control should give very low to no activity.

If the positive control does not produce the expected result, there may be something wrong with the experimental procedure, and the experiment is repeated. For difficult or complicated experiments, the result from the positive control can also help in comparison to previous experimental results. For example, if the well-established disease test was determined to have the same effect as found by previous experimenters, this indicates that the experiment is being performed in the same way that the previous experimenters did.

When possible, multiple positive controls may be used—if there is more than one disease test that is known to be effective, more than one might be tested. Multiple positive controls also allow finer comparisons of the results (calibration, or standardization) if the expected results from the positive controls have different sizes. For example, in the enzyme assay discussed above, a standard curve may be produced by making many different samples with different quantities of the enzyme.

Randomization[edit]

In randomization, the groups that receive different experimental treatments are determined randomly. While this does not ensure that there are no differences between the groups, it ensures that the differences are distributed equally, thus correcting for systematic errors.

For example, in experiments where crop yield is affected (e.g. soil fertility), the experiment can be controlled by assigning the treatments to randomly selected plots of land. This mitigates the effect of variations in soil composition on the yield.

Blind experiments[edit]

Blinding is the practice of withholding information that may bias an experiment. For example, participants may not know who received an active treatment and who received a placebo. If this information were to become available to trial participants, patients could receive a larger placebo effect, researchers could influence the experiment to meet their expectations (the observer effect), and evaluators could be subject to confirmation bias. A blind can be imposed on any participant of an experiment, including subjects, researchers, technicians, data analysts, and evaluators. In some cases, sham surgery may be necessary to achieve blinding.

During the course of an experiment, a participant becomes unblinded if they deduce or otherwise obtain information that has been masked to them. Unblinding that occurs before the conclusion of a study is a source of experimental error, as the bias that was eliminated by blinding is re-introduced. Unblinding is common in blind experiments and must be measured and reported. Meta-research has revealed high levels of unblinding in pharmacological trials. In particular, antidepressant trials are poorly blinded. Reporting guidelines recommend that all studies assess and report unblinding. In practice, very few studies assess unblinding.^[3]

Blinding is an important tool of the scientific method, and is used in many fields of research. In some fields, such as medicine, it is considered essential.^[4] In clinical research, a trial that is not blinded trial is called an open trial.

See also[edit]

• False positives and false negatives
• Designed experiment
• Controlling for a variable
• James Lind cured scurvy using a controlled experiment that has been described as the first clinical trial.^[5][6]
• Wait list control group

References[edit]

External links[edit]

• «Control» . Encyclopædia Britannica. Vol. 7 (11th ed.). 1911.

A scientific control is an experiment or observation designed to minimize the effects of variables other than the independent variable (i.e. confounding variables). This increases the reliability of the results, often through a comparison between control measurements and the other measurements.

What is the control in a science test?

When conducting an experiment, a control is an element that remains unchanged or unaffected by other variables. It is used as a benchmark or a point of comparison against which other test results are measured.

What is a control in science example?

An example of a control in science would be cells that get no treatment in an experiment. Say there is a scientist testing how a new drug causes cells to grow. One group, the experimental group would receive the drug and the other would receive a placebo. The group that received the placebo is the control group.

What is control variable science?

A control variable is anything that is held constant or limited in a research study. It’s a variable that is not of interest to the study’s aims, but is controlled because it could influence the outcomes.

What is the role of a control in an experiment?

Controls allow the experimenter to minimize the effects of factors other than the one being tested. It’s how we know an experiment is testing the thing it claims to be testing. This goes beyond science — controls are necessary for any sort of experimental testing, no matter the subject area.

How do you find the control?

Control limits are calculated by:

1. Estimating the standard deviation, σ, of the sample data.
2. Multiplying that number by three.
3. Adding (3 x σ to the average) for the UCL and subtracting (3 x σ from the average) for the LCL.

What does controlling for mean?

means that a particular variable is kept the same or nearly the same when comparing other variables. For example, if you were to look at risky occupations in general, there will be some people with high levels of education and some with low.

What are 3 controlled variables?

Controlled variable: the height of the slope, the car, the unit of time e.g. minutes and the length of the slope. What you can decide to change in an experiment.

What is a control in biology?

In scientific experiments, a scientific control is one in which the subject or a group would not be tested for the dependent variable(s). The inclusion of a control in an experiment is crucial for generating conclusions from the empirical data.

What is a scientific control group?

The control group is composed of participants who do not receive the experimental treatment. When conducting an experiment, these people are randomly assigned to be in this group. They also closely resemble the participants who are in the experimental group or the individuals who receive the treatment.

Why use a control in an experiment?

The word “control” has a number of meanings in science, but just as long as you hear a “positive” in front of it, you can know immediately what it means in microbiology: an experiment that contains a repetition of itself, only with a treatment known to work. Even though this technical definition might sound confusing, the idea of a positive control is relatively intuitive: a positive control is a duplicate experiment that helps microbiologists confirm the correctness of their experiments and results.

“Control” Is a Confusing Word

Ask a child what a control is and he’ll likely point to the TV remote. Ask a statistician the same question, and he’ll tell you it’s a variable that can cause problems in an experiment. But ask a microbiologist and she’ll tell you that a control is a duplicate experiment run with a different experimental group of subjects or treatments. According to the College of Charleston, microbiologists see controls as necessary, using them to check the findings of a certain experiment against those that have already produced results.

Addition and Subtraction: What’s the Difference?

Controls come in two flavors: positive and negative. A negative control is a controlled experiment that microbiologists know will have a negative outcome, whereas a positive control is an experiment that microbiologists know will have a positive outcome. These controls allow for comparisons for the new experiment, helping a microbiologist check new results against already known results.

For example, a microbiologist testing the effectiveness of a new soap in killing bacteria could run an experiment on whether the soap works, but she won’t know if it truly works without comparing the results against those in an experimental group using soap known to work, and against those in an experimental group using no soap, which definitely won’t work.

A Microbiology Positive Control Example: Far Removed from Your TV

In microbiology, a scientist often runs a new experiment twice: once to find out results and a second time to compare the results. She will usually run the experiments simultaneously.

For example, a microbiologist who wishes to check the effect of a new soap on killing germs might run one sample of germs under soapy water, checking the amount of germs killed afterward. She would create the “positive control” version of the experiment by replacing the first soapy water with soapy water made from a soap that she knows to work in killing bacteria. Running the experiment again will produce results that might differ from the results of the first experiment.

What’s the Point? Logic!

Checking a new treatment against a positive control is both a way of checking for effects and for checking for problems in an experiment. Logically, if a new treatment, such as a new liquid soap, produces results similar to the old treatment, a bar of soap, then the scientist can conclude that the new method works. This way of performing a controlled experiment has the added benefit of allowing a microbiologist to immediately compare two different treatments.

What’s the Point? Troubleshooting

In other situations, a microbiologist might find a problem in her controlled experiment after looking at the positive control’s results. For example, she might see that the new soap kills less than 10% of the bacteria and conclude that the soap is not effective.

But if she checks this result against a soap proved to work, she might find that the “proven” soap also only kills less than 10% of the bacteria. From here, she might conclude that the experiment has a problem and rework her experiment.

In mathematics, to control a mathematical object is to limit its behavior in an attempt to eliminate pathologies. Usually, what one does is to make a certain hypothesis on a mathematical object and then see what restrictions it imposes on the object.

In your case, the assumption of monotone convergence transforms pointwise convergence into uniform convergence. In general, the pointwise limit of a sequence of continuous $ mathbb{R} $-valued functions on a topological space is a pretty wild animal. When the topological space is a metric space, we call such a function a ‘Baire-$ 1 $ function’. Baire-$ 1 $ functions can be hard to visualize. However, with the assumption of monotone convergence, the pointwise limit of a sequence of continuous $ mathbb{R} $-valued functions on a compact space will always be a nice continuous $ mathbb{R} $-valued function. One can thus say that the assumption of monotone convergence controls the behavior of pointwise limits.

Professor Terence Tao uses the word ‘control’ a lot because in almost all of his works, in order to prevent the branching complexity of his arguments from growing beyond his already-formidable powers of analysis, he usually has to make restricting hypotheses at certain stages.

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