Understanding statistics and statistical myths : how to become a profound learner

Myth 1: Two Types of Data Attribute/Discrete and Measurement/Continuous. Myth 2: Proportions and Percentages Are Discrete Data. Myth 3: s = v[S(Xi- x)2/(n- 1)] The Correct Formula for Sample Standard Deviation. Myth 4: Sample Standard Deviation v[S(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 T