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Questions and Answers
What does an odds ratio of 1 indicate about two groups being compared?
What does an odds ratio of 1 indicate about two groups being compared?
Which of the following best describes the significance of log odds in statistical analysis?
Which of the following best describes the significance of log odds in statistical analysis?
In the Vit C study, what odds was calculated for the placebo group?
In the Vit C study, what odds was calculated for the placebo group?
What condition is indicated by a log odds ratio estimate of 0?
What condition is indicated by a log odds ratio estimate of 0?
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What does the standard error (se) formula approximate in the context of log odds analysis?
What does the standard error (se) formula approximate in the context of log odds analysis?
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What is the primary characteristic of a Prospective Binomial sample?
What is the primary characteristic of a Prospective Binomial sample?
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Which sampling model is primarily used for case-control studies?
Which sampling model is primarily used for case-control studies?
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What does the term 'odds' refer to in the context of Odds Ratios?
What does the term 'odds' refer to in the context of Odds Ratios?
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In which sampling model is the total number of subjects fixed?
In which sampling model is the total number of subjects fixed?
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When using Odds Ratios to compare two proportions, what issue is avoided?
When using Odds Ratios to compare two proportions, what issue is avoided?
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What does the formula Odds = π/(1 − π) calculate?
What does the formula Odds = π/(1 − π) calculate?
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Which of the following statements about Retrospective Binomial sampling is accurate?
Which of the following statements about Retrospective Binomial sampling is accurate?
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What is a potential disadvantage of using simple differences in proportions across populations?
What is a potential disadvantage of using simple differences in proportions across populations?
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What is the consequence of violating the assumption of independence in statistical analysis?
What is the consequence of violating the assumption of independence in statistical analysis?
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When is it suggested to use t-tests in the presence of data assumptions?
When is it suggested to use t-tests in the presence of data assumptions?
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Which type of data would require the use of non-parametric tests?
Which type of data would require the use of non-parametric tests?
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What is the role of transforming responses in statistical methods?
What is the role of transforming responses in statistical methods?
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What is indicated by the term 'Bernoulli data' in relation to statistical models?
What is indicated by the term 'Bernoulli data' in relation to statistical models?
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Under what circumstances should a researcher consider using a randomization test?
Under what circumstances should a researcher consider using a randomization test?
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In the context of equality of two proportions, which situation is described by the Vit C study example?
In the context of equality of two proportions, which situation is described by the Vit C study example?
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What important aspect must be considered regarding sample sizes when checking for equality of variance?
What important aspect must be considered regarding sample sizes when checking for equality of variance?
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Study Notes
Statistical Methods Key Points
- Assumptions are crucial: Understanding assumptions of statistical tests and the consequences of violating them (e.g., independence, equal variance, normality) is vital. Incorrect assumptions lead to inaccurate p-values and confidence intervals.
- Method Selection: No single best method exists; the optimal approach depends on the research question, study design, and data characteristics.
- Question-Driven Approach: Start with the research question, then determine appropriate study design, collect data, and choose an appropriate analysis.
- Independence: Data points should be independent of each other—crucial to avoid bias and inaccurate results.
- Data Types: Distinguish between experimental and observational studies (causal conclusions depend on the design).
- Paired or Independent Data: Paired data relate observations within groups; unpaired data involves independent observations.
- Assumptions Evaluation: Assess whether data meet assumptions (reasonable or not seriously violated) before choosing a test.
Hypothesis Testing and Model Comparison
- Model Comparison (T-tests): Model I assumes a shared mean, while Model II assumes different means. Use the model with the better fit to test the hypotheses.
- Chi-Square Test: Employed when comparing proportions across groups (e.g., treatment and control).
- Expected/Observed counts: compare the expected counts to observed values.
Bernoulli and Binomial Distributions
- Bernoulli Data: Discrete data where a single event has two possible outcomes (i.e., success or failure).
- Binomial Data: Represents the number of successes out of a given number of trials (using the Bernoulli distribution).
Sampling Models
- Prospective Binomial: Observations are collected before the outcome is known.
- Retrospective Binomial: Data are collected after the outcome is known. Often used in case-control studies when outcomes are uncommon.
- Multinomial: Data involve observations categorized into multiple groups.
Odds Ratios
- Odds Ratio Calculation: Used to compare the odds of an event between different groups (e.g., Vit C and Placebo).
- Log Odds Ratio: Useful for analyzing differences in odds of proportions across several populations with diverse rates.
Inference for Log Odds Ratios
- Log Odds Ratio Estimate: The log odds ratio is calculated to quantify the relationship between outcomes in the two groups. This is then used to estimate a confidence interval for the ratio.
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Description
This quiz covers essential key points regarding statistical methods and their assumptions. Understanding how to select the appropriate method based on the research question and data characteristics is crucial for accurate analysis. Key concepts like independence, data types, and the impact of assumptions are discussed.
