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When a manipulation creates significant differences between experimental conditions in both (1) the dependent variable and (2) the measured manipulation check variable, the interpretation is that (1) the manipulation "causes" variation in the dependent variable (the "effect") and (2) the manipulation also explains variation in some other, more ...
Inferential statistics cannot separate variability due to treatment from variability due to experimental units when there is only one measurement per unit. Sacrificial pseudoreplication (Figure 5b in Hurlbert 1984) occurs when means within a treatment are used in an analysis, and these means are tested over the within unit variance.
A clinical control group can be a placebo arm or it can involve an old method used to address a clinical outcome when testing a new idea. For example in a study released by the British Medical Journal, in 1995 studying the effects of strict blood pressure control versus more relaxed blood pressure control in diabetic patients, the clinical control group was the diabetic patients that did not ...
In the statistical theory of the design of experiments, blocking is the arranging of experimental units that are similar to one another in groups (blocks) based on one or more variables. These variables are chosen carefully to minimize the affect of their variability on the observed outcomes.
In a randomized trial (i.e., an experimental study), the average treatment effect can be estimated from a sample using a comparison in mean outcomes for treated and untreated units. However, the ATE is generally understood as a causal parameter (i.e., an estimate or property of a population ) that a researcher desires to know, defined without ...
Difference in differences (DID [1] or DD [2]) is a statistical technique used in econometrics and quantitative research in the social sciences that attempts to mimic an experimental research design using observational study data, by studying the differential effect of a treatment on a 'treatment group' versus a 'control group' in a natural experiment. [3]
In the statistical theory of design of experiments, randomization involves randomly allocating the experimental units across the treatment groups.For example, if an experiment compares a new drug against a standard drug, then the patients should be allocated to either the new drug or to the standard drug control using randomization.
The number of treatment units (subjects or groups of subjects) assigned to control and treatment groups, affects an RCT's reliability. If the effect of the treatment is small, the number of treatment units in either group may be insufficient for rejecting the null hypothesis in the respective statistical test.