Overview of Hypothesis Testing

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Compare Sample to Population • Is 25 enough? • Next lecture we will discuss • What difference in cholesterol l is clinically or biologically meaningful? • Have an available sample and want to know if hypertensives are different than general population p
Situation • May be you are reading another person’s work • May be already collected data • If you were designing up front you would calculate l the sample size • But for now, we have 25 people p
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: One Sample: Cholesterol l Sample Da
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 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 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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