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Principles of Hypothesis Testing fo
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Objectives • Formulate questions
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Estimation and Hypotheses ‣Infere
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You Use Hypothesis Testing • Desi
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Analysis Follows Design Questions
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Estimation and Hypotheses Inference
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Pictures, Not Numbers • Scatter p
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Like the Washington Post Weather, T
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Distributions • Parametric tests
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Binary Distribution • Binomial di
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Estimation and Hypotheses Inference
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Null Hypothesis • For superiority
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Example Hypotheses • H 0 : μ 1 =
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Use a 2-Sided Test • Almost alway
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Outline Estimation and Hypotheses
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Information at Hand • 1 or 2 samp
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One Sample: Cholesterol l Sample Da
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Situation • May be you are readin
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Cholesterol Sample Data • Populat
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Test Statistic • Basic test stati
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Unknown Truth and the Data Data Tru
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Type II Error (or, 1- Power) • β
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Cholesterol Sample Data • N = 25
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Experiment Develop hypotheses Colle
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Z or Standard Normal Distributionib
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How to test? ‣Rejection interval
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Cholesterol Rejection Interval Usin
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Side Note on t vs. Z • If s = σ
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Z-test: Do Not Reject H 0 Z X −
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Determining Statistical Significanc
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Cholesterol: t-statistic X − μ 2
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How to test? Rejection interval j
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Cholesterol Example • P-value for
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P-value Interpretation Reminders
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Outline Estimation and Hypotheses H
- Page 71 and 72: Cholesterol Confidence Interval Usi
- Page 73 and 74: Cholesterol Confidence Interval Usi
- Page 75 and 76: Hypothesis Testing and dConfidence
- Page 77 and 78: Interpret a 95% Confidence Interval
- Page 79 and 80: Take Home: Hypothesis Testing • M
- Page 81 and 82: Take Home: CI • Meaning/interpret
- Page 83 and 84: Outline Estimation and Hypotheses H
- Page 85 and 86: Linear regression • Model for sim
- Page 87 and 88: More Than One Covariate • Y i =
- Page 89 and 90: Repeated Measures (3 or more time p
- Page 91 and 92: Do Not Use Correlation. Use Regress
- Page 93 and 94: Outline Estimation and Hypotheses H
- Page 95 and 96: Omics • False negative (Type II e
- Page 97 and 98: What do you need to think about?
- Page 99 and 100: Little Diagnostic Testing Lingo •
- Page 101 and 102: Example: Western vs. ELISA • 1 mi
- Page 103 and 104: 1% Prevalence • 10980 total test
- Page 105 and 106: 10% Prevalence • 99% PPV • 99.9
- Page 107 and 108: Prevalence Matters • PPV and NPV
- Page 109 and 110: What do you need to think about?
- Page 111 and 112: Avoid Common Mistakes: Hypothesis T
- Page 113 and 114: Avoid Common Mistakes: Hypothesis T
- Page 115 and 116: Comparing A to B • Appropriate
- Page 117 and 118: 3 Studies. 3 Answers, Maybe • Stu
- Page 119 and 120: Misconceptions • A small p-value
- Page 121: Questions?
- Page 125 and 126: 2 Samples: Same Variance + Sample S
- Page 127 and 128: One Sample Binary Outcomes • Exac
- Page 129 and 130: Normal/Large Sample Data? No Binomi