CK-12 Advanced Probability and Statistics by CK-12 Foundation

Author:CK-12 Foundation
Language: eng
Format: azw, mobi
Publisher: CK-12 Foundation
Published: 2010-09-09T16:00:00+00:00

Lesson Summary

The important elements of a statistical experiment are randomness, imposed treatments, and replication. These elements are the only effective method for establishing meaningful cause and effect relationships. An experiment attempts to isolate, or control other potential variables to may contribute to changes in the response variable. If these other variables are known quantities but are difficult, or impossible, to distinguish from the other explanatory variables, they are called confounding variables. If there is an additional explanatory variable affecting the response variable that was not considered in an experiment, it is called a lurking variable. A treatment is the term used to refer to a condition imposed on the subjects in an experiment. An experiment will have at least two treatments. When trying to test the effectiveness of a particular treatment, it is often effective to withhold applying that treatment to a group of randomly chosen subjects. This is called a control group. If the subjects are aware of the conditions of their treatment, they may have preconceived expectations that could affect the outcome. Especially in medical experiments, the psychological effect of believing you are receiving a potentially effective treatment can lead to different results. This phenomenon is called the placebo effect. When the participants in a clinical trial are led to believe they are receiving the new treatment, when in fact they are not, it is called a placebo. If the participants are not aware of the treatment they are receiving, it is called a blind experiment. When neither the participant nor the researcher are aware of which subjects are receiving the treatment and which subjects are receiving a placebo, it is called a double-blind experiment.

Blocking is a technique used to control the potential confounding of variables. It is similar to the idea of stratification in sampling. In a randomized block design, the researcher creates blocks of subjects that exhibit similar traits which might cause different responses to the treatment and then randomly assigns the different treatments within each block. A matched pairs design is a special type of design when there are two treatments. The researcher creates blocks of size two on some similar characteristic and then randomly assigns one subject from each pair to each treatment. Repeated measures designs are a special matched pairs experiment in which each subject becomes it’s own matched pair by applying both treatments and comparing the results.

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