STA 210 Lecture Notes - Lecture 1: Dependent And Independent Variables, Confounding, Design Of Experiments

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16 Feb 2017
Part 1
An experiment is when you collect data under controlled conditions with the goal of
establishing something close to cause and effect
Control separates experiments from collecting a survey
Control is what makes an experiment an experiment
Confounding is purposeful control produces some of the purest data one can collect
Confounding is to confuse or to make up
In statistics, confusion caused by a third variable distorting the association being studied
between two other variables
There are two sources of confounding:
Inadequate or improper comparison
Lack of randomization
Response variable- the primary variable you are taking measurements on for your
Explanatory variable- what you are varying in your experiment (different treatments or
treatment levels)
Subjects- Who or what you are doing the experiment on
Lurking variable- another name for that third variable that can cause confounding
Placebo effect-real response from subjects to an inert treatment
Part 2
Benefit of randomization
Helps to keep the comparison groups as much alike as possible
Randomization in some sense addresses confounding in a very direct way
The placebo effect and lack of randomization can create very real obstacles to making
credible inferences from experimental data
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