Negative control5/2/2023 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. Positive controls are often used to assess test validity. The treatment is only effective if the treatment group shows more improvement than 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). Even if the treatment group shows improvement, it needs to be compared to the placebo group. 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. In this case, the treatment is inferred to have no effect when the treatment group and the negative control produce the same results. In the drug testing example, we could measure the percentage of patients cured. In other examples, outcomes might be measured as lengths, times, percentages, and so forth. 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. 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. Where there are only two possible outcomes, e.g. 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. The simplest types of control are negative and positive controls, and both are found in many different types of experiments. 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. For example, it may be necessary to use a tractor to spread fertilizer where there is no other practicable way to spread fertilizer. Controls are most often necessary where a confounding factor cannot easily be separated from the primary treatments. Now the experiment is controlled for the dilutant and the experimenter can distinguish between sweetener, dilutant, and non-treatment. 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. For instance, the artificial sweetener might be mixed with a dilutant and it might be the dilutant that causes the effect. Other variables, which may not be readily obvious, may interfere with the experimental design. 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.įor 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. The selection and use of proper controls to ensure that experimental results are valid (for example, absence of confounding variables) can be very difficult. 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. See also: Scientific method and Experimental designĬontrols eliminate alternate explanations of experimental results, especially experimental errors and experimenter bias.
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