- Simple linear regression.
- Multiple linear regression.
- Multiple logistic regression.
- Poisson regression.
No category found.
- Bar chart.
- Pie chart.
- Histogram.
- Scatter plot.
- To immediately stop the trial because of small benefits.
- To weigh the statistical significance against the clinical relevance and potential harms, recognizing that a statistically significant effect may not be clinically meaningful.
- To continue the trial regardless of clinical significance.
- To only consider the p-value.
- Alternative hypothesis.
- Research hypothesis.
- Null hypothesis.
- Statistical hypothesis.
- Nominal.
- Ordinal.
- Discrete.
- Continuous.
- The null hypothesis of no effect is likely true.
- The null hypothesis of no effect can be rejected because the interval does not include zero.
- The drug is harmful.
- The confidence interval is too wide.
- Independent samples t-test.
- Paired t-test.
- Chi-square test.
- One-way ANOVA.
- Paired t-test.
- Chi-square test.
- Independent samples t-test.
- One-way ANOVA.
- It makes it easier to recruit participants.
- It often necessitates a very large sample size to detect a meaningful effect, which can be challenging to achieve.
- Sample size is irrelevant for rare diseases.
- It means the drug will always be effective.
- Simple linear regression.
- Multiple linear regression.
- T-test.
- Chi-square test.
- Widely dispersed.
- Identical.
- Normally distributed.
- Heavily skewed.
- To immediately impute all missing values.
- To investigate the pattern of missingness and assess its potential impact on bias and the validity of results.
- To ignore the missing values if they are less than 5%.
- To discard all records with missing values.
- The p-value is too high.
- The study had too much power.
- Loss to follow-up can introduce bias and threaten the internal validity of the study.
- The drug is definitely effective.
- Independent samples t-test.
- Paired t-test.
- Chi-square test of independence.
- One-way ANOVA.
- Descriptive statistics.
- Inferential statistics.
- Data visualization.
- Data collection.
- Histogram.
- Scatter plot.
- Bar chart.
- Line graph.
- Simple linear regression.
- Multiple linear regression.
- Logistic regression.
- Poisson regression.
- Statistical significance always implies clinical significance.
- Statistical significance does not always imply clinical significance.
- The study had too many participants.
- The p-value is too low.
