Confounding and the Language of ExperimentationPart III – Statistical Significance

This video is designed to accompany

pages 13-18in

Making Sense of UncertaintyActivities for Teaching Statistical

ReasoningVan-Griner Publishing Company

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.

Flibanserin

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 : http://www.time.com/time/health/article/0,8599,1939884,00.html#ixzz1rvoYBIax

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: http://www.time.com/time/health/article/0,8599,1939884,00.html#ixzz1rvoYBIax

Was the Flibanserin treatment effective?

Statistical Significance

DefinitionStatistical 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.

One-Sentence Reflection

Credible inferences from experimental data have to ultimately be held to a formal standard of statistical significance.