What is no correlation
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Correlation is one of two major means of conducting a study. The other is experimentation. In most cases, experimentation is preferred because the experimenter is able to manipulate the variable of interest and directly measure the outcome. For example, a researcher looking at the influence of rowing on weight loss can determine the exact time and technique of rowing and then measure the outcome after a set amount of time; also, the researcher is able to control for extraneous variables that could affect the outcome. Correlational research, on the other hand, has no such control.
Correlation measures the relationship between two variables. Unlike in experimentation, the relationship is observed in a more natural environment. There's no experimenter to control variables; rather, variables interact outside of the laboratory. Because there's no experimenter to control how variables interact, no correlational study can determine how a phenomenon is caused. This has led to the important mantra that correlation doesn't necessarily show causation. Nonetheless, for broad but still meaningful observations, correlative findings can provide great insight.
Correlational research allows researchers
to collect much more data than experiments. Furthermore, because correlational research usually takes place outside of the lab, the results tend to be more applicable to everyday life. Another benefit of correlational research is that it opens up a great deal of further research to other scholars. When researchers begin investigating a phenomenon or relationship for the first time, correlational research provides a good starting position. It allows researchers to determine the strength and direction of a relationship so that later studies can narrow the findings down and, if possible, determine causation experimentally.
Correlation research only uncovers a relationship; it cannot provide a conclusive reason for why there's a relationship. A correlative finding doesn't reveal which variable influences the other. For example, finding that wealth correlates highly with education doesn't explain whether having wealth leads to more education or whether education leads to more wealth. Reasons for either can be assumed, but until more research is done, causation can't be determined. Also, a third, unknown variable might be causing both. For instance, living in the state of New York can lead to both wealth and education.Source: ehow.com