Overview of Hypothesis Testing

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Appendix • Formulas for Critical Values • Layouts for how to choose a test
Do Not Reject H 0 σ 46 220=X > μ 0 + 1.96 =211+1.96* = 228.03 NO! n 25 σ 46 220=X < μ 0 − 1.96 = 211-196* 1.96 = 192.9797 NO! n 25
Page 1 and 2:
Principles of Hypothesis Testing fo
Page 3 and 4:
Objectives • Formulate questions
Page 5 and 6:
Estimation and Hypotheses ‣Infere
Page 7 and 8:
You Use Hypothesis Testing • Desi
Page 9 and 10:
Analysis Follows Design Questions
Page 11 and 12:
Estimation and Hypotheses Inference
Page 13 and 14:
Pictures, Not Numbers • Scatter p
Page 15 and 16:
Like the Washington Post Weather, T
Page 17 and 18:
Distributions • Parametric tests
Page 19 and 20:
Binary Distribution • Binomial di
Page 21 and 22:
Estimation and Hypotheses Inference
Page 23 and 24:
Null Hypothesis • For superiority
Page 25 and 26:
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
Page 39 and 40:
Test Statistic • Basic test stati
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Unknown Truth and the Data Data Tru
Page 43 and 44:
Type II Error (or, 1- Power) • β
Page 45 and 46:
Cholesterol Sample Data • N = 25
Page 47 and 48:
Experiment Develop hypotheses Colle
Page 49 and 50:
Z or Standard Normal Distributionib
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How to test? ‣Rejection interval
Page 53 and 54:
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
Page 67 and 68:
P-value Interpretation Reminders
Page 69 and 70:
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
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Overview of Hypothesis Testing

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