16 Questions
What is the power of a statistical test when the probability of Type II error is 20%?
80%
What is the primary purpose of power analysis?
To determine the sample size required for a study
What is the minimal clinically important difference (MCID)?
The smallest meaningful change perceived by patients
What is the purpose of a pilot study in power analysis?
To obtain estimates of the effect size
What is the relationship between alpha and Type I error?
Alpha is the probability of Type I error
What is the purpose of power analysis in a study?
To determine the required sample size for a study
What is the effect size in power analysis?
A measure of the difference between groups
How many types of power analysis are there?
4
What is a necessary step to take before choosing a statistical test for quantitative data?
Checking for normality
What is the primary purpose of a one-sample t-test?
To compare the mean of a sample to a standard value
Which of the following designs is an example of a paired design?
Split-mouth design
What is the primary distinction between a paired t-test and a Wilcoxon signed-rank test?
The assumption of normality
Which statistical test is used to compare the means of more than two groups?
One-way ANOVA
What is the purpose of checking for homogeneity of variances among groups?
To meet the assumption of equal variances for certain statistical tests
Which statistical test is used to compare the means of two independent groups?
Independent t-test
What is the purpose of a Mann-Whitney U test?
To compare the distributions of two independent groups
Study Notes
Power Analysis
- Power analysis involves four parameters: alpha (α), power, sample size (n), and effect size (ES).
- Alpha (α) is typically set to 0.05.
- Power is equal to 100% - Beta (β), and is often set to 80% (100 - 20).
Types of Power Analysis
- There are four types of power analysis: a priori, post-hoc, criterion, and sensitivity.
- A priori analysis computes the sample size (n) given alpha, power, and effect size (ES).
- Post-hoc analysis computes the power given alpha, sample size (n), and effect size (ES).
- Criterion analysis computes alpha given power, effect size (ES), and sample size (n).
- Sensitivity analysis computes the effect size (ES) given alpha, power, and sample size (n).
Effect Size
- Effect size quantifies the differences between groups.
- Minimal clinically important difference (MCID) is the smallest meaningful change or the smallest difference in score that patients perceive as beneficial.
Obtaining Effect Size
- Effect size can be obtained through pilot study results, published findings, field-defined meaningful effects, or expert opinion.
Tests for Quantitative Data
- Before choosing a statistical test, check for normality and homogeneity of variances among groups.
- Tests for quantitative data include:
- One-sample t-test (compares with a standard value)
- Paired t-test (parametric, compares before and after values)
- Wilcoxon signed-rank test (non-parametric, compares before and after values)
- Independent t-test (parametric, compares two groups)
- Mann-Whitney U test (non-parametric, compares two groups)
- One-way ANOVA (parametric, compares more than two groups)
- Kruskal-Wallis test (non-parametric, compares more than two groups)
Designs for Quantitative Data
- Paired designs include split-mouth design and matched pairs.
This quiz covers the basics of power analysis, statistical tests for one and two group comparisons, and their applications in biostatistics and healthcare quality management.
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