Chapter 1- Introduction to Data

The treatment group are the individuals who receive

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Unformatted text preview: effected by some treatment. The Treatment Group are the individuals who receive the treatment The Control Group are the individuals who do not receive the treatment. Example: To see if red wine decreases the chance of heart disease, 800 red wine drinkers and 900 non red wine drinkers were observed. Professor: Dai-Trang Le Chapter 1 – Introduction to Data January 8, 2014 26 / 32 Collecting Data to Understand Causality Randomized Experiment versus Observational Studies Two reasons why we must sometimes use an observational study instead of an experiment: 1 2 It is unethical or impossible to assign people to receive a specific treatment. Certain explanatory variables, such as handedness, are inherent traits and cannot be randomly assigned. Professor: Dai-Trang Le Chapter 1 – Introduction to Data January 8, 2014 27 / 32 Collecting Data to Understand Causality Association is not Causation Unless the individuals of the study are identical in every way except for the treatment, we cannot conclude that the treatment caused the outcome. In an observational study, association does not imply causation. People with grey hair are observed to have more wrinkles. This does not mean that grey hair causes wrinkles. A confounding variable is a characteristic other than the treatment that causes both outcomes. For example, old age causes both grey hair and wrinkles. Professor: Dai-Trang Le Chapter 1 – Introduction to Data January 8, 2014 28 / 32 Collecting Data to Understand Causality Anecdotal Evidence is not Science An Anecdote is a story that a single individual tells about his or her own experience. An anecdote should never be used to make a statement about a group of individuals. If Jack drank peanut juice and notices that he became much healthier, that does not mean that peanut juice makes people healthy. Professor: Dai-Trang Le Chapter 1 – Introduction to Data January 8, 2014 29 / 32 Collecting Data to Understand Causality Resource for Learning Statistics http://homepages.luc.edu/~thoover/statistics/websites.htm Professor: Dai-Trang Le Chapter 1 – Introduction to Data January 8, 2014 30 / 32 Collecting Data to Understand Causality Recommended Readings 13 data milestones for 2013: http://www.pewresearch.org/facttank/2013/12/23/13-data-milestones-for-2013/ Professor: Dai-Trang Le Chapter 1 – Introduction to Data January 8, 2014 31 / 32 Collecting Data to Understand Causality Next Lecture Chapter 2: Displaying and Summarizing Data Professor: Dai-Trang Le Chapter 1 – Introduction to Data January 8, 2014 32 / 32...
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