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Questions and Answers
What is a primary outcome of understanding bias in Evidence-Based Medicine (EBM)?
What is a primary outcome of understanding bias in Evidence-Based Medicine (EBM)?
Which type of bias results from how a study population is selected?
Which type of bias results from how a study population is selected?
Why is recognizing bias crucial for clinicians in EBM?
Why is recognizing bias crucial for clinicians in EBM?
What does a confidence interval measure in research?
What does a confidence interval measure in research?
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Which of the following best describes a Type I error?
Which of the following best describes a Type I error?
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What is the result of publication bias in clinical research?
What is the result of publication bias in clinical research?
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What is one possible effect of poor understanding of bias in EBM?
What is one possible effect of poor understanding of bias in EBM?
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Which of the following statistics is used to quantify the power of a study?
Which of the following statistics is used to quantify the power of a study?
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What is the primary feature of confounding bias?
What is the primary feature of confounding bias?
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How can confounding bias be minimized in studies?
How can confounding bias be minimized in studies?
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What distinguishes effect modification from confounding bias?
What distinguishes effect modification from confounding bias?
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What is an example of lead-time bias?
What is an example of lead-time bias?
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Why is length-time bias significant in screening tests?
Why is length-time bias significant in screening tests?
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What does publication bias entail?
What does publication bias entail?
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What does intention to treat analysis maintain in a clinical trial?
What does intention to treat analysis maintain in a clinical trial?
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What is a major limitation of intention to treat analysis?
What is a major limitation of intention to treat analysis?
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What strategy is NOT commonly used to correct for confounding bias?
What strategy is NOT commonly used to correct for confounding bias?
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What does the median represent in a dataset with an even number of values?
What does the median represent in a dataset with an even number of values?
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Which statement is true regarding modes in a dataset?
Which statement is true regarding modes in a dataset?
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What characteristic is NOT associated with a normal distribution?
What characteristic is NOT associated with a normal distribution?
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What does the 68-95-99.7 rule describe in a normal distribution?
What does the 68-95-99.7 rule describe in a normal distribution?
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What is the primary limitation of using the range as a measure of variability?
What is the primary limitation of using the range as a measure of variability?
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In a bimodal distribution, what does each peak represent?
In a bimodal distribution, what does each peak represent?
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What aspect does the normal distribution signify in terms of data occurrence?
What aspect does the normal distribution signify in terms of data occurrence?
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Which description is false about the mode?
Which description is false about the mode?
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What defines a non-normal distribution?
What defines a non-normal distribution?
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Which statement best explains the primary advantage of nonparametric tests?
Which statement best explains the primary advantage of nonparametric tests?
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What is the main purpose of the Chi-Square test in statistical analysis?
What is the main purpose of the Chi-Square test in statistical analysis?
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Which of the following statements about p-values is accurate?
Which of the following statements about p-values is accurate?
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Which of the following best defines clinical significance?
Which of the following best defines clinical significance?
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Which of these tests is NOT considered a nonparametric test?
Which of these tests is NOT considered a nonparametric test?
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Which statement best describes the relationship between variance and the data points in relation to the mean?
Which statement best describes the relationship between variance and the data points in relation to the mean?
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What is the primary purpose of generating confidence intervals?
What is the primary purpose of generating confidence intervals?
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Which confidence level is most commonly used by convention in statistical analysis?
Which confidence level is most commonly used by convention in statistical analysis?
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What role does hypothesis testing play in evidence-based medicine (EBM)?
What role does hypothesis testing play in evidence-based medicine (EBM)?
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What is the null hypothesis (H0) generally indicative of?
What is the null hypothesis (H0) generally indicative of?
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In hypothesis testing, what is typically controlled to minimize inappropriate treatment decisions?
In hypothesis testing, what is typically controlled to minimize inappropriate treatment decisions?
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What happens to the confidence interval as the sample size increases?
What happens to the confidence interval as the sample size increases?
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Why is avoiding Type I errors particularly crucial in EBM?
Why is avoiding Type I errors particularly crucial in EBM?
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Variance is expressed in which of the following units?
Variance is expressed in which of the following units?
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Which statement best describes the significance of p-values in hypothesis testing?
Which statement best describes the significance of p-values in hypothesis testing?
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Study Notes
Importance of Bias in EBM
- Understanding and minimizing bias improves the quality of research by obtaining more accurate results.
- Recognizing bias enhances patient care by ensuring better informed treatment decisions.
- Bias informs clinicians on how to interpret results and critically evaluate the strength of evidence.
- It contributes to transparency and accountability in research by increasing the integrity of the medical literature.
Bias Definitions
- Bias is a deviation of results or inferences from the truth.
- Bias is introduced during study design, data collection, data analysis, and publication.
Bias - Recruitment & Follow-up
- Selection Bias is introduced during patient selection.
- Confounding Bias is when a characteristic related to the exposure and outcome distorts the effect of an exposure on an outcome.
- Confounding Bias can be minimized through randomization, cross-over studies, matching, and stratification and can be corrected with statistical adjustment.
- Lead-Time Bias occurs when early detection appears to increase survival, but the course of the disease remains unchanged.
- Length-Time Bias occurs when a screening test detects diseases with a long latency period, while those with a shorter latency become symptomatic faster.
Publication Bias
- A.K.A. Study Selection Bias. Research with significant or positive findings are more likely to be published than research with non-significant or negative results.
- It is a form of bias that can affect systematic reviews and meta-analyses.
Intention To Treat Analysis
- Subjects are analyzed based on their initial group assignment regardless of drop-out or non-adherence.
- This approach minimizes the effect of attrition and non-adherence but may dilute the actual intervention effects.
Measures of Central Tendency
- Mean.
- Median.
- Mode.
- Range.
Normal Distribution
- A.K.A. Gaussian distribution is characterized by symmetrical, bell-shaped curve.
- The mean, median, and mode are equal in a perfectly normal distribution.
- The 68-95-99.7 rule states that approximately 68% of the data falls within one standard deviation, 95% fall within two standard deviations, and 99.7% fall within three standard deviations.
Non-Normal Distribution
- Bimodal distribution has two distinct peaks or modes.
- Important in data analysis to recognize as affected by the choice of statistical methods and interpretations, particularly when normality is not met.
Confidence Intervals
- A range of values around a calculated statistic for a sample, believed to contain the true value of that statistic within a specific probability.
- The larger the sample size, the narrower the range.
Hypothesis Testing
- A structured framework used to evaluate the effectiveness of medical interventions.
- Guides practitioners to choose treatments backed by statistical evidence.
- Controls for type I and type II errors.
- Statistical rigor is ensured by setting predefined thresholds (p-values), standardizing the process of determining if observed effects are statistically significant.
Null Hypothesis
- Assumes that two possibilities are the same.
- There is no statistical difference observed in a set of observations.
- This hypothesis is deemed "true" until proven wrong by experimental data.
Nonparametric Tests
- fewer assumptions about the underlying data; more robust in cases of non-normality or heteroscedasticity.
- Suitable for analyzing ordinal data or non-continuous variables.
- Characteristics:
- No normality assumption in the population distribution.
- Capable of handling ordinal, ranked, or categorical data.
- Applicable when sample sizes are small, outliers are present, or the data has non-constant variance.
Example of Nonparametric Tests
- Chi-Square: tests the association between two categorical variables.
- Fisher’s Exact Test: tests association between two categorical variables in small sample sizes.
- Mann-Whitney U test: compares differences between two independent groups; nonparametric equivalent of the t-test.
- Wilcoxon signed-rank test: a nonparametric alternative to the paired t-test.
- Kruskal-Wallis test: compares three or more independent groups; nonparametric equivalent of ANOVA.
Statistical Significance
- Refers to the likelihood that an observed effect in a study is due to something other than chance, typically determined using a p-value.
- Helps determine if the results of a study have statistical meaning.
- A statistically significant result means there is strong evidence against the null hypothesis suggesting that the observed effect is unlikely to occur randomly.
Clinical Significance
- Refers to the practical importance of a treatment effect and the actual benefits to a patient.
- Evaluates the effect size and its potential impact on a patient's health or quality of life.
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Description
This quiz explores the critical role of bias in evidence-based medicine (EBM) research. Discover how recognizing and minimizing bias not only improves research quality but also enhances patient care and treatment decisions. Learn about different types of bias and their implications for clinical practice.