Understanding Statistics and Statistical Myths: How to Become a Profound Learner

Addressing 30 statistical myths in the areas of data, estimation, measurement system analysis, capability, hypothesis testing, statistical inference, and control charts, this book explains how to understand statistics rather than how to do statistics. Every statistical myth listed in this book has been stated in course materials used by the author’s clients, by employers, or by experts in training thousands. Each myth is an unconditional statement that, when taken literally and at face value, is false. All are false under some conditions while a few are not true under any condition. This book explores the conditions that render false the universality of the statements to help you understand why. In the book, six characters discuss various topics taught in a fictional course intended to teach students how to apply statistics to improve processes. The reader follows along and learns as the students apply what they learn to a project in which they are team members. Each discussion is like a Platonic dialogue. The purpose of a Platonic dialogue is to analyze a concept, statement, hypothesis, or theory through questions, applications, examples, and counterexamples, to see if it is true, when it is true, and why it is true when it is true. The dialogues will help readers understand why certain statements are not always true under all conditions, as well as when they contradict other myths.

Table of Contents:
Myth 1: Two Types of Data—Attribute/Discrete and Measurement/Continuous. Myth 2: Proportions and Percentages Are Discrete Data. Myth 3: s = √[Σ(Xi- x)2/(n- 1)] The Correct Formula for Sample Standard Deviation. Myth 4: Sample Standard Deviation √[Σ(Xi-x)2/(n- 1)] Is Unbiased. 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 Ho. 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.

About the Author :
Kicab Castaneda-Mendez, founder of Process Excellence Consultants, Chapel Hill, NC, provides consulting and training on operational excellence using lean Six Sigma methodologies, balanced scorecard and Baldrige framework.