Castañeda-Méndez K. Understanding Statistics and Statistical Myths: How to Become a Profound Learner

New York: CRC Press, 2016. — 523 p. — ISBN: 978-1-4987-2746-4.Addressing more than 30 statistical myths, this book explains how to understand statistics rather than how to do statistics. In the book, six characters have 30 discussions on various topics taught in a fictional course intended to teach students how to apply statistics to improve processes. Readers follow along and learn as the students apply what they learn to a project in which they are team members. Each discussion will helps readers understand why certain statements are not always true under all conditions, as well as when they contradict other myths.Myth 1: Two Types of Data—Attribute/Discrete and Measurement/Continuous
Myth 2: Proportions and Percentages Are Discrete Data
Myth 3: s = √ ⎡ (xi − x)2 /(n−1) 1 n ∑ ⎡ Is the Correct
Myth 4: Sample Standard Deviation
Myth 5: Variances Can Be Added but Not Standard Deviations
Myth 6: Parts and Operators for an MSA Do Not Have to Be Randomly Selected
Myth 7: % Study (% Contribution, Number of Distinct Categories) Is the Best Criterion for Evaluating a Measurement System for Process Improvement
Myth 8: Only Sigma Can Compare Different Processes and Metrics
Myth 9: Capability Is Not Percent/Proportion of Good Units
Myth 10: p = Probability of Making an Error
Myth 11: Need More Data for Discrete Data than Continuous Data Analysis
Myth 12: Nonparametric Tests Are Less Powerful than Parametric Tests
Myth 13: Sample Size of 30 Is Acceptable (for Statistical Significance)
Myth 14: Can Only Fail to Reject Ho, Can Never Accept H_σ
Myth 15: Control Limits Are ±3 Standard Deviations from the Center Line
Myth 16: Control Chart Limits Are Empirical Limits
Myth 17: Control Chart Limits Are Not Probability Limits
Myth 18: ±3 Sigma Limits Are the Most Economical Control Chart Limits
Myth 19: Statistical Inferences Are Inductive Inferences
Myth 20: There Is One Universe or Population If Data Are Homogeneous
Myth 21: Control Charts Are Analytic Studies
Myth 22: Control Charts Are Not Tests of Hypotheses
Myth 23: Process Needs to Be Stable to Calculate Process Capability
Myth 24: Specifications Don’t Belong on Control Charts
Myth 25: Identify and Eliminate Assignable or Assignable Causes of Variation
Myth 26: Process Needs to Be Stable before You Can Improve It.
Myth 27: Stability (Homogeneity) Is Required to Establish a Baseline
Myth 28: A Process Must Be Stable to Be Predictable
Myth 29: Adjusting a Process Based on a Single Defect Is Tampering, Causing Increased Process Variation
Myth 30: No Assumptions Required When the Data Speak for Themselves
Epilogue