- 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.
Author: ETEA MCQS.COM
No category found.
- 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.
- Linear regression.
- Logistic regression.
- Poisson regression.
- Survival regression.
- 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.
- Independent samples t-test.
- Paired t-test.
- Chi-square test.
- One-way ANOVA.
- 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.
- 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.
- Two-tailed test.
- One-tailed (right-tailed) test.
- One-tailed (left-tailed) test.
- Non-directional test.
- Paired t-test.
- Independent samples t-test.
- Chi-square test.
- One-way ANOVA.
- To assume the correlation is accurate.
- To urgently explain that measurement error can attenuate (reduce) the observed correlation, potentially masking a stronger true relationship.
- To ignore the measurement error.
- To only focus on the correlation coefficient.
- Inferential statistics.
- Descriptive statistics.
- Hypothesis testing.
- Sampling.
- Superiority test.
- Non-inferiority test.
- Equivalence test.
- Association test.
- To use the data without any changes.
- To urgently highlight potential for information bias due to inconsistent data collection and to develop strategies to assess and mitigate this bias.
- To assume data are perfect.
- To only analyze a subset of the data.
- Histogram.
- Scatter plot.
- Bar chart.
- Line graph.
- 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-way ANOVA.
