Module 7 Help Session F18-1.pdf

Module 7 Help Session F18-1.pdf - PHC 4069 Biostatistics in...

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PHC 4069 Biostatistics in Society Module 7 hypotheses and confidence intervals Prepared By: Hanze Zhang Presented By: Ying Ma
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Two types of statistics Statistics Descriptive Collecting, organizing Summarizing, presenting Inferential Hypotheses Relationships predictions
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Two types of statistics Statistics Descriptive Collecting, organizing Summarizing, presenting Inferential Hypotheses Relationships predictions Probability
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Definition of hypothesis testing Hypothesis: a scientific guess. Usually about a given characteristic of a given population Ex: Frequent alcohol drinkers have higher systolic blood pressure (SBP) than the average. Use inferential statistics to test this guess
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Hypothesis testing Obtain a representative sample from the population we are trying to infer about. Define null and alternative hypotheses Example null hypothesis: The mean SBP of frequent alcohol drinkers is 115 mmHg Example alternative (research) hypothesis: The mean SBP of frequent alcohol drinkers is higher than 115 mmHg. Analyze the evidence from the sample Sample mean Is there significant difference between the sample statistic and the hypothesized parameter to reject the null hypothesis? or are the observed differences due to random sampling variation?
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Hypothesis testing Analyze the evidence from the sample: Is there significant difference between the sample statistic and the hypothesized parameter to reject the null hypothesis? or are the observed differences due to random sampling variation? What’s the probability of seeing the observed sample mean if the null hypothesis is true? If the probability is low enough, then we reject the null hypothesis
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Level of significance If the probability of observing the obtained data, given that the null hypothesis is true , is lower than the alpha level , then we reject the null hypothesis This is called a p-value Alpha level is usually set at 0.05 (5%)
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Two types of errors We are inferring from a sample, mistakes can happen: Type-I error: reject the null hypothesis when it is true.
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