- Discrete variables.
- Nominal variables.
- Continuous variables.
- Ordinal variables.
No category found.
- 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.
- The intercept.
- The error term.
- The predicted value of Y.
- The slope, representing the change in Y for a one-unit change in X.
- To only focus on the p-value.
- To urgently explain that while statistically significant, the absolute increase is only 5 percentage points, which might be less impressive than the relative increase, and to frame findings transparently.
- To ignore the absolute difference.
- To assume all significant findings are large.
- Two-tailed test.
- One-tailed (right-tailed) test.
- One-tailed (left-tailed) test.
- Non-directional test.
- Independent samples t-test.
- Paired t-test.
- Chi-square test.
- One-sample t-test.
- Parameter.
- Population.
- Statistic.
- Hypothesis.
- To accept the questionnaire as is.
- To urgently advise that an unvalidated instrument can lead to unreliable and invalid data, compromising the study's conclusions, and to recommend using a validated tool.
- To only focus on the sample size.
- To assume the questionnaire is good.
- The null hypothesis of no effect can be rejected because the interval does not include zero.
- The null hypothesis of no effect is likely true.
- The drug is harmful.
- The confidence interval is too wide.
- P-value.
- T-statistic.
- Correlation coefficient.
- Regression coefficient.
- To immediately celebrate the program's success.
- To urgently explain that this is a misleading metric and that a proper comparison with unvaccinated individuals or a calculation of vaccine effectiveness is critically needed.
- To ignore the unvaccinated group.
- To assume correlation implies effectiveness.
- Linear regression.
- Logistic regression.
- Poisson regression.
- Survival regression.
- Independent samples t-test.
- Paired t-test.
- Chi-square test.
- One-way ANOVA.
- The number of variables in the study.
- The number of observations in the sample minus the number of parameters estimated.
- The significance level.
- The power of the test.
- To ignore the dropouts as normal.
- To urgently assess if the dropout is differential (related to outcome or treatment), as this can introduce selection bias and invalidate the study results.
- To only analyze complete cases.
- To recruit more participants.
- Two-tailed test.
- One-tailed (right-tailed) test.
- One-tailed (left-tailed) test.
- Non-directional test.
