Experimental methods are finding increasing use in manufacturing to optimize the production process. Specifically, the goal of these methods is to identify the optimum settings for the different factors

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Experimental methods are widely used in research as well as in industrial settings, however, sometimes for very different purposes. The primary goal in scientific research is usually to show the statistical significance of an effect that a particular factor exerts on the dependent variable of interest.

Experimental design is a planned interference in the natural order of events by the researcher. He does something more than carefully observe what is occurring. This emphasis on experiment reflects the higher regard generally given to information so derived. There is good rationale for this. Much of the substantial gain in knowledge in all sciences has come from actively manipulating or interfering with the stream of events. There is more than just observation or measurement of a natural event. A selected condition or a change (treatment) is introduced. Observations or measurements are planned to illuminate the effect of any change in conditions.

The importance of experimental design also stems from the quest for inference about causes or relationships as opposed to simply description. Researchers are rarely satisfied to simply describe the events they observe. They want to make inferences about what produced, contributed to, or caused events. To gain such information without ambiguity, some form of experimental design is ordinarily required. As a consequence, the need for using rather elaborate designs ensues from the possibility of alternative relationships, consequences or causes. The purpose of the design is to rule out these alternative causes, leaving only the actual factor that is the real cause.

Causal-comparative research is a useful tool that can be employed in situations where experimental designs are not possible.  The researcher must remember, however, that demonstrating a relationship between two variables (even a very strong relationship) does not “prove” that one variable actually causes the other to change.

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The given article describes an experiment held with a random assignment of people to groups and statistical controls for confounding. The purpose of the research was to explore how people think about and act to manage future risks of product failure. Besides that, the researchers tried to investigate the key variables associated with risk management strategies.

Extraneous variables (those that may influence or affect the results of the treatment on the subject) were used. A constant variable was introduced through clearly specified outcome probabilities. Independent variables, which are variants of decisions the respondents had to make, were established by ...

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