- Standard deviation.
- Range.
- Variance.
- Interquartile range.
No category found.
- Paired t-test.
- Independent samples t-test.
- Chi-square test.
- One-way ANOVA.
- Independent samples t-test.
- Paired t-test.
- Mann-Whitney U test (or Wilcoxon rank-sum test).
- ANOVA.
- Significance level.
- Confidence level.
- Power.
- P-value.
- Range.
- Interquartile range.
- Standard deviation.
- Mode.
- High power is not necessary.
- High statistical power (e.g., 80% or 90%) is critically needed.
- The power should be as low as possible.
- Power is only relevant for large differences.
- Nominal.
- Ordinal.
- Discrete.
- Continuous.
- Binomial distribution.
- Poisson distribution.
- Normal distribution.
- Uniform distribution.
- Histogram.
- Scatter plot.
- Pie chart or bar chart.
- Line graph.
- The drug definitely increases the biomarker.
- The drug definitely decreases the biomarker.
- The confidence interval includes zero, suggesting no statistically significant effect of the drug on the biomarker.
- The drug is highly effective.
- Matching.
- Stratification.
- Randomization.
- Blinding.
- Descriptive statistics.
- Probability.
- Inferential statistics.
- Data visualization.
- Independent samples t-test.
- Paired t-test.
- Chi-square test.
- One-way ANOVA.
- Small sample size makes the finding more reliable.
- Small sample size can lead to unstable estimates and findings that may not replicate in larger studies.
- The p-value is always accurate regardless of sample size.
- The study had high power.
- Histogram.
- Scatter plot.
- Kaplan-Meier curve.
- Box plot.
- Sample.
- Statistic.
- Population.
- Parameter.
- The null hypothesis assumes the new drug is inferior by a pre-specified margin.
- The null hypothesis assumes the new drug is superior.
- The significance level is ignored.
- Non-inferiority trials do not use hypothesis testing.
