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Summary: Observational and Experimental Research

Observational research consists of data collection without experiment­ation. Data analysis may reveal patterns called correlations.

Correlations can be used to make predictions. Computers using large amounts of correlational data can make predictions more accurate than expert humans.

The pitfalls of observational research involve biasing effects that introduce inaccuracies into the data. Measurement and observer effects occur when data is distorted by the act of collecting it. Population validity problems occur when results obtained with one group do not apply accurately to a different group.

Leading questions, biasing context, and willingness of respondents to give opinions without any background knowledge are all factors that can distort the accuracy of a survey. Telephone polling has been tainted by push polls that attempt to influence opinion while pretending to gather data

Observational research produces evidence of correlations, and correlations can be used to make predictions. This is true even if we do not know the reason for the correlation. The only requirement for prediction is that a correlation lasts into the future.

Experimental research is used to test cause-effect claims. In experimental research, an independent variable is manipulated and dependent variables are measured.

In a typical between-subjects experi­mental design, one group receives a genuine experimental manipulation while another group receives a placebo treatment. A single-blind design, in which a participant does not know whether a treatment is real or placebo, controls the placebo effect. To prevent bias from experimenter effects as well as placebo effects, a double-blind procedure is required.

Human intuition is based on detecting patterns (correlations). This provides expectations about what will happen in the future. Observational research, properly interpreted, extends or amplifies these powers of prediction.

Experimental research allows us to an analyze systems into their components and how they interact. This extends the human power of analysis and makes it possible for us to understand and repair natural systems.

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