1. Confounding and the Language of Experimentation
Part III – Statistical Significance 1

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

3. Confounding Aside Even if confounding is not an issue …
There’s a larger challenge to the inferences that are being made.
Let’s look at an example. 3

4. Flibanserin 4

5. Randomized, Placebo Trial
The Flibanserin findings are based on the study of 1,378 premenopausal women who had been in a monogamous relationship for 10 years on average. The women were randomly assigned to take 100 mg of Flibanserin or a placebo daily and to record daily whether they had sex and whether it was satisfying. The women were screened for depression and other medical conditions, and all had a diagnosis of HSDD. From : 5

6. Making a Credible Inference
Women in the Flibanserin group self-reported 2.8 sexually satisfying events in the four-week baseline period; in the final four weeks of the 24-week study period, those women reported 4.5 sexually satisfying events, a more than 50% increase. Women in the placebo group reported an increase from 2.7 events to 3.7.
From:
Was the Flibanserin treatment effective? 6

7. Statistical Significance
Definition
Statistical Significance - A difference between treatments that is sufficiently large that it is unlikely to have occurred by chance alone.
This is a formal, probabilistic statement made possible by some of the wonderful mathematical science on which statistical science resides.
In this module we won’t address the mathematics, just the idea. 7

8. One-Sentence Reflection
Credible inferences from experimental data have to ultimately be held to a formal standard of statistical significance.