1. Confounding and the Language of Experimentation Part I - Introduction

2. This video is designed to accompany pages 13-18 in Making Sense of Uncertainty Activities for Teaching Statistical Reasoning Van-Griner Publishing Company

3. Experiment ex·per·i·ment 1.A test under controlled conditions that is made to demonstrate a known truth, examine the validity of a hypothesis, or determine the efficacy of something previously untried. 2.The process of conducting such a test; experimentation. Statistical Experimentation?

4. Controlled Conditions Fundamentally different than a survey in that you make a concerted effort to control the conditions under which the data are collected. The goal is to infer from the data collected cause and effect between what you are allowing to vary as part of your experiment and what you are measuring as your outcomes.

5. Confounding This purposeful control produces some of the purest data one can collect. In this course we don’t talk much about how experiments are designed – a rich statistical topic. Instead we focus more on how “confounding” can compromise inferences about cause and effect even with data that are so carefully collected.

6. Vernacular con·found [ kən fównd ] bewilder somebody: to puzzle or confuse somebody make things worse: to cause a confused situation to become even more confused Synonyms: confuse, muddle, mix up, mistake, misperceive

7. Statistical English confounding [konfoun′ding] interference by a third variable so as to distort the association being studied between two other variables, because of a strong relationship with both of the other variables. a relationship between two causal factors such that their individual contributions cannot be separated.

8. Sources of Confounding We will need some language to make sense of all this, but for our limited take on confounding in this class we consider two sources only: 1.Inadequate or improper comparison 2.Lack of randomization

9. Language 1.Response variable – The primary variable you are taking measurements on for your experiment. 2.Explanatory variable – What you are varying in your experiment (different treatments or treatment levels). 3.Subjects – Who or what you are doing the experiment on. 4.Lurking variable – Another name for that third variable that can cause confounding. 5.Placebo Effect – Real response from subjects to an inert treatment.

10. One-Sentence Reflection Credible inferences from experimental data have to be free from confounding, which has two primary sources – lack of proper comparison and improper randomization of subjects to treatments.