- The probability of making a Type II error.
- The probability of correctly rejecting the null hypothesis.
- The threshold below which a p-value is considered statistically significant.
- The power of the test.
No category found.
- To proceed with the small sample size.
- To urgently advise that an underpowered study is unethical and a waste of resources, as it has a high chance of missing a true effect (Type II error).
- To only consider the cost of the trial.
- To assume the drug will work anyway.
- There is very strong evidence against the null hypothesis.
- There is weak evidence against the null hypothesis.
- The null hypothesis is true.
- The drug is harmful.
- Independent samples t-test.
- Paired t-test.
- Chi-square test.
- One-way ANOVA.
- Population distribution.
- Sample distribution.
- Sampling distribution of the mean.
- Normal distribution.
- To accept the mean as the best measure.
- To urgently recommend using the median as a more robust measure of central tendency for skewed data to avoid misrepresenting the typical income.
- To ignore the skewness.
- To only use the mode.
- Equivalence test.
- Non-inferiority test.
- Superiority test.
- Association test.
- Independent samples t-test.
- Paired t-test.
- Chi-square test of independence.
- One-way ANOVA.
- Range.
- Standard deviation.
- Variance.
- Interquartile range.
- To immediately recommend the drug.
- To urgently explain that while statistically significant, the clinical relevance of such a small effect size is questionable and might not justify the drug's cost or side effects.
- To only focus on the p-value.
- To assume statistical significance equals clinical importance.
- Linear regression.
- Logistic regression.
- Poisson regression.
- Cox proportional hazards regression.
- Independent samples t-test.
- Paired t-test.
- Chi-square test.
- One-way ANOVA.
- Analysis of Variables.
- Analysis of Variance.
- Analysis of Values.
- Analysis of Variation.
- To accept the strong correlation as is.
- To urgently explain that Pearson's correlation coefficient only measures linear association, and a non-linear relationship might be missed or misinterpreted.
- To ignore the scatter plot.
- To assume all relationships are linear.
- One-tailed (right-tailed) test.
- One-tailed (left-tailed) test.
- Two-tailed test.
- Non-directional test.
- Chi-square test.
- Independent samples t-test.
- Regression analysis (likely linear, but could explore other distributions if warranted).
- One-way ANOVA.
- Discrete variables.
- Nominal variables.
- Continuous variables.
- Ordinal variables.
- To immediately accept the results.
- To urgently highlight the potential for observer bias and the need for objective outcome measures or blinded assessment to ensure validity.
- To disregard the p-value.
- To assume clinician assessment is always objective.
- Independent samples t-test.
- Paired t-test.
- Z-test for two proportions or Chi-square test.
- One-way ANOVA.
