Statistical Thinking – PSYC 100_ Principles of Psychology F21.pdf

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5. Statistical Thinking
Original chapter by Beth Chance and Allan Rossman adapted by the Queen’s
University Psychology Department
This Open Access chapter was originally written for the NOBA project.
Information on the NOBA project can be found below.
We encourage students to use the “Three-Step Method” for support in their
learning. Please ±nd our version of the Three-Step Method, created in collabo-
ration with Queen’s Student Academic Success Services, at the following link:
As our society increasingly calls for evidence-based decision making, it is im-
portant to consider how and when we can draw valid inferences from data.
This module will use four recent research studies to highlight key elements of
a statistical investigation.

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Learning Objectives
De±ne basic elements of a statistical investigation.
Describe the role of p-values and con±dence intervals in statistical
inference.
Describe the role of random sampling in generalizing conclusions from a
sample to a population.
Describe the role of random assignment in drawing cause-and-effect
conclusions.
Critique statistical studies.
Introduction
Does drinking coffee actually increase your life expectancy? A recent study (
Freedman, Park, Abnet, Hollenbeck, & Sinha, 2012 ) found that men who
drank at least six cups of coffee a day had a 10% lower chance of dying
(women 15% lower) than those who drank none. Does this mean you should
pick up or increase your own coffee habit?
Modern society has become awash in studies such as this; you can read about
several such studies in the news every day. Moreover, data abound everywhere
in modern life. Conducting such a study well, and interpreting the results of
such studies well for making informed decisions or setting policies, requires
understanding basic ideas of statistics, the science of gaining insight from
data. Rather than relying on anecdote and intuition, statistics allows us to sys-
tematically study phenomena of interest.

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