Design of experiments
The experimental design may also identify control variables that must be held constant to prevent external factors from affecting the results.
Experimental design involves not only the selection of suitable independent, dependent, and control variables, but planning the delivery of the experiment under statistically optimal conditions given the constraints of available resources.
For example, these concerns can be partially addressed by carefully choosing the independent variable, reducing the risk of measurement error, and ensuring that the documentation of the method is sufficiently detailed.
[5] Charles S. Peirce randomly assigned volunteers to a blinded, repeated-measures design to evaluate their ability to discriminate weights.
[24] False positive conclusions, often resulting from the pressure to publish or the author's own confirmation bias, are an inherent hazard in many fields.
[25] Use of double-blind designs can prevent biases potentially leading to false positives in the data collection phase.
[26] Experimental designs with undisclosed degrees of freedom[jargon] are a problem,[27] in that they can lead to conscious or unconscious "p-hacking": trying multiple things until you get the desired result.
[26] Clear and complete documentation of the experimental methodology is also important in order to support replication of results.
[32] An experimental design or randomized clinical trial requires careful consideration of several factors before actually doing the experiment.
Some of the following topics have already been discussed in the principles of experimental design section: The independent variable of a study often has many levels or different groups.
Thus, when everything else except for one intervention is held constant, researchers can certify with some certainty that this one element is what caused the observed change.
In the pure experimental design, the independent (predictor) variable is manipulated by the researcher – that is – every participant of the research is chosen randomly from the population, and each participant chosen is assigned randomly to conditions of the independent variable.
Investigators should ensure that uncontrolled influences (e.g., source credibility perception) do not skew the findings of the study.
One of the most important requirements of experimental research designs is the necessity of eliminating the effects of spurious, intervening, and antecedent variables.
Developments of the theory of linear models have encompassed and surpassed the cases that concerned early writers.
Some important contributors to the field of experimental designs are C. S. Peirce, R. A. Fisher, F. Yates, R. C. Bose, A. C. Atkinson, R. A. Bailey, D. R. Cox, G. E. P. Box, W. G. Cochran, W. T. Federer, V. V. Fedorov, A. S. Hedayat, J. Kiefer, O. Kempthorne, J.
A. Nelder, Andrej Pázman, Friedrich Pukelsheim, D. Raghavarao, C. R. Rao, Shrikhande S. S., J. N. Srivastava, William J. Studden, G. Taguchi and H. P.
[41][42] Laws and ethical considerations preclude some carefully designed experiments with human subjects.
Constraints may involve institutional review boards, informed consent and confidentiality affecting both clinical (medical) trials and behavioral and social science experiments.
[43] In the field of toxicology, for example, experimentation is performed on laboratory animals with the goal of defining safe exposure limits for humans.
