Learning from Data: A Short Course

What it’s about

Learning From Data: An Introduction to Statistical Reasoning teaches readers a new way of thinking about and learning from data across psychological, social, educational, political, and economic domains. Rather than treating statistics as mere number-crunching, the book emphasizes the logic underlying each procedure: why variability matters, how sampling distributions enable inference, and how the six-step hypothesis-testing schema applies from the simplest z-test to complex factorial ANOVA. Built around two real datasets (a smoking-cessation study and a maternity/marital-satisfaction study), the book devotes a full chapter to each difficult concept, uses extensive repetition, and integrates parametric and nonparametric procedures so students learn to choose the right tool. It uniquely confronts the gap between random sampling (which statistics textbooks preach) and random assignment (which experiments actually use), giving readers the conceptual tools to question and challenge data-based claims and to conduct sound research themselves.

The through-line

Who it’s for

A student or researcher in the behavioral sciences who wants to understand, conduct, and critically evaluate data-based claims about the world.

The problem

Data in the behavioral sciences are messy and variable, making it impossible to see clear facts without proper analysis, and the reader must choose and apply the right statistical procedure. The reader feels intimidated by statistics, fears the math, and worries they cannot tell good data from misleading claims.

The plan

1. Learn to describe data with frequency distributions, central tendency, variability, and z scores.
2. Understand probability and sampling distributions as the foundation of inference.
3. Master the six-step hypothesis-testing schema and apply it across procedures.
4. Use the Statistical Selection Guide to pick the right test for any situation.
5. Distinguish random sampling from random assignment to know what you can conclude.

The payoff

The reader can choose and correctly apply the appropriate statistical procedure for any situation. · The reader can interpret data and their limitations, drawing conclusions only about the populations sampled. · The reader can critically question and challenge data-based claims in everyday life.

See our guide

• The Four-S Discipline — One System, Four Capabilities →
• Read The Numbers Honestly — Uncertainty, Base Rates, And Noise →
• Choose And Apply The Right Analytical Method →
• Ship Analytics, Don't Just Prototype It →
• Practice Business Intelligence & Data Science →
• Get The Business To Actually Use It →
• Build A Model That Holds Up Outside Your Sample →
• Get The Data Trustworthy First →

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Additional reading

• Silent Scourge · C. F. Moore

  Cited in Chapter 1 as an excellent discussion on the societal dangers of not collecting sufficient data on specific populations, such as children, when setting standards for environmental pollutants.

• Psychological Testing · A. Anastasi

  Referenced in Chapter 1 as a classic text that discusses the concepts of validity and reliability in psychological measurement in detail.

• Randomization Tests · S. E. Eddington

  Mentioned in Chapter 5 as a resource for inferential procedures specifically designed for situations where random sampling is not used, which is common in psychology experiments.

• Introductory Statistics for the Behavioral Sciences · J. Welkowitz, R. B. Ewen, and J. Cohen

  Cited in Chapter 9 for providing a suggested compromise for setting levels of alpha and beta in research (α = .05 and power = .80) balancing scientific caution with the need to make new discoveries.

Featured in these guides

• Ship Analytics, Don't Just Prototype It →Cited source
• Practice Business Intelligence & Data Science →Cited source
• Get The Business To Actually Use It →Cited source
• Build A Model That Holds Up Outside Your Sample →Cited source
• Read The Numbers Honestly — Uncertainty, Base Rates, And Noise →Cited source
• Choose And Apply The Right Analytical Method →Cited source
• Get The Data Trustworthy First →Cited source