<P> A particular problem with observational studies involving human subjects is the great difficulty attaining fair comparisons between treatments (or exposures), because such studies are prone to selection bias, and groups receiving different treatments (exposures) may differ greatly according to their covariates (age, height, weight, medications, exercise, nutritional status, ethnicity, family medical history, etc .). In contrast, randomization implies that for each covariate, the mean for each group is expected to be the same . For any randomized trial, some variation from the mean is expected, of course, but the randomization ensures that the experimental groups have mean values that are close, due to the central limit theorem and Markov's inequality . With inadequate randomization or low sample size, the systematic variation in covariates between the treatment groups (or exposure groups) makes it difficult to separate the effect of the treatment (exposure) from the effects of the other covariates, most of which have not been measured . The mathematical models used to analyze such data must consider each differing covariate (if measured), and results are not meaningful if a covariate is neither randomized nor included in the model . </P> <P> To avoid conditions that render an experiment far less useful, physicians conducting medical trials--say for U.S. Food and Drug Administration approval--quantify and randomize the covariates that can be identified . Researchers attempt to reduce the biases of observational studies with complicated statistical methods such as propensity score matching methods, which require large populations of subjects and extensive information on covariates . Outcomes are also quantified when possible (bone density, the amount of some cell or substance in the blood, physical strength or endurance, etc .) and not based on a subject's or a professional observer's opinion . In this way, the design of an observational study can render the results more objective and therefore, more convincing . </P> <P> By placing the distribution of the independent variable (s) under the control of the researcher, an experiment--particularly when it involves human subjects--introduces potential ethical considerations, such as balancing benefit and harm, fairly distributing interventions (e.g., treatments for a disease), and informed consent . For example, in psychology or health care, it is unethical to provide a substandard treatment to patients . Therefore, ethical review boards are supposed to stop clinical trials and other experiments unless a new treatment is believed to offer benefits as good as current best practice . It is also generally unethical (and often illegal) to conduct randomized experiments on the effects of substandard or harmful treatments, such as the effects of ingesting arsenic on human health . To understand the effects of such exposures, scientists sometimes use observational studies to understand the effects of those factors . </P> <P> Even when experimental research does not directly involve human subjects, it may still present ethical concerns . For example, the nuclear bomb experiments conducted by the Manhattan Project implied the use of nuclear reactions to harm human beings even though the experiments did not directly involve any human subjects . </P>

When is an experiment said to be complete