Confounding and the Language of Experimentation

Part I - Introduction

This video is designed to accompany

pages 13-18

in

Making Sense of UncertaintyActivities for Teaching Statistical

ReasoningVan-Griner Publishing Company

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?

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.

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.

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

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.

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

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.

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.