The Classical Experimental Design Helps Guard Again
ane.6: Experiments
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Studies where the researchers assign treatments to cases are called experiments. When this assignment includes randomization, e.k. using a money ip to determine which treatment a patient receives, information technology is called a randomized experiment. Randomized experiments are fundamentally important when trying to show a causal connection betwixt two variables.
Principles of experimental blueprint
Randomized experiments are generally congenital on four principles.
Decision-making. Researchers assign treatments to cases, and they do their best to control whatsoever other differences in the groups. For example, when patients have a drug in pill form, some patients take the pill with simply a sip of water while others may take information technology with an unabridged drinking glass of water. To control for water consumption, a md may ask all patients to beverage a 12 ounce drinking glass of water with the pill.
Randomization. Researchers randomize patients into treatment groups to account for variables that cannot be controlled. For example, some patients may exist more susceptible to a disease than others due to their dietary habits. Randomizing patients into the treatment or control grouping helps even out such differences, and information technology likewise prevents accidental bias from entering the study.
Replication. The more cases researchers observe, the more accurately they can gauge the effect of the explanatory variable on the response. In a single study, nosotros replicate by collecting a sufficiently large sample. Additionally, a grouping of scientists may replicate an entire study to verify an earlier nding.
Blocking. Researchers sometimes know or doubtable that variables, other than the handling, inuence the response. Nether these circumstances, they may rst group individuals based on this variable into blocks and and so randomize cases within each block to the treatment groups. This strategy is often referred to as blocking. For instance, if nosotros are looking at the effect of a drug on heart attacks, we might rst separate patients in the study into low-risk and high-take chances blocks, then randomly assign half the patients from each block to the control group and the other one-half to the treatment grouping, as shown in Effigy 1.xv. This strategy ensures each treatment grouping has an equal number of depression-risk and high-run a risk patients.
Information technology is important to incorporate the rst three experimental design principles into any report, and this book describes applicative methods for analyzing data from such experiments. Blocking is a slightly more than advanced technique, and statistical methods in this book may exist extended to analyze data collected using blocking.
Reducing bias in man experiments
Randomized experiments are the gold standard for information collection, but they do not ensure an unbiased perspective into the crusade and outcome relationships in all cases. Human studies are perfect examples where bias tin can unintentionally ascend. Here nosotros reconsider a report where a new drug was used to treat heart attack patients.17 In detail, researchers wanted to know if the drug reduced deaths in patients.
17Anturane Reinfarction Trial Research Group. 1980. Sul npyrazone in the prevention of sudden death after myocardial infarction. New England Periodical of Medicine 302(5):250-256.
These researchers designed a randomized experiment considering they wanted to describe causal conclusions virtually the drug's effect. Study volunteers18 were randomly placed into two study groups. I group, the treatment grouping, received the drug. The other grouping, called the control grouping, did not receive any drug handling.
Put yourself in the place of a person in the study. If yous are in the treatment group, you are given a fancy new drug that you conceptualize will help y'all. On the other hand, a person in the other group doesn't receive the drug and sits idly, hoping her participation doesn't increase her risk of death. These perspectives suggest there are actually ii effects: the one of interest is the effectiveness of the drug, and the 2nd is an emotional effect that is difficult to quantify.
Researchers aren't commonly interested in the emotional effect, which might bias the report. To circumvent this problem, researchers do non desire patients to know which group they are in. When researchers keep the patients uninformed about their treatment, the report is said to be bullheaded. Only there is one problem: if a patient doesn't receive a treatment, she will know she is in the control grouping. The solution to this problem is to requite fake treatments to patients in the control group. A fake treatment is called a placebo, and an effective placebo is the key to making a study truly bullheaded. A classic example of a placebo is a sugar pill that is made to await like the actual treatment pill. Oft times, a placebo results in a slight simply real improvement in patients. This consequence has been dubbed the placebo effect.
The patients are not the only ones who should be blinded: doctors and researchers tin accidentally bias a study. When a doctor knows a patient has been given the real treatment, she might inadvertently give that patient more than attending or intendance than a patient that she knows is on the placebo. To guard against this bias, which again has been found to have a measurable effect in some instances, nigh modernistic studies utilize a double-blind setup where doctors or researchers who collaborate with patients are, just like the patients, unaware of who is or is not receiving the treatment.19
Do 1.14 Look back to the report in Section 1.1 where researchers were testing whether stents were effective at reducing strokes in at-chance patients. Is this an experiment? Was the study blinded? Was it double-blinded?xx
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