Overview of Hypothesis Testing

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Is α or β more important ? • Depends on the question • Most will say protect against Type I error • Multiple comparisons • Need to think about individual and population health implications and costs
Omics • False negative (Type II error) • Miss what could be important • Are these samples going to be looked at again? • False positive (Type I error) • Waste resources following dead ends
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 =
Page 27 and 28:
Use a 2-Sided Test • Almost alway
Page 29 and 30:
Outline Estimation and Hypotheses
Page 31 and 32:
Information at Hand • 1 or 2 samp
Page 33 and 34:
One Sample: Cholesterol l Sample Da
Page 35 and 36:
Situation • May be you are readin
Page 37 and 38:
Cholesterol Sample Data • Populat
Page 39 and 40:
Test Statistic • Basic test stati
Page 41 and 42:
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
Page 51 and 52: How to test? ‣Rejection interval
Page 53 and 54: Cholesterol Rejection Interval Usin
Page 55 and 56: Side Note on t vs. Z • If s = σ
Page 57 and 58: Z-test: Do Not Reject H 0 Z X −
Page 59 and 60: Determining Statistical Significanc
Page 61 and 62: Cholesterol: t-statistic X − μ 2
Page 63 and 64: How to test? Rejection interval j
Page 65 and 66: 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: Outline Estimation and Hypotheses H
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 and 122: Questions?
Page 123 and 124: Do Not Reject H 0 σ 46 220=X > μ
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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