- 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.
Author: ETEA MCQS.COM
No category found.
- Simple linear regression.
- Multiple linear regression.
- T-test.
- Chi-square test.
- Widely dispersed.
- Identical.
- Normally distributed.
- Heavily skewed.
- 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.
- 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.
- 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.
- 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.
- Simple linear regression.
- Multiple linear regression.
- Logistic regression.
- Poisson regression.
- The probability of making a Type I error is 20%.
- The probability of correctly rejecting the null hypothesis when it is false is 80%.
- The probability of making a Type II error is 80%.
- The significance level is 0.20.
- The new drug is definitely better.
- There is no statistically significant difference between the two treatments.
- The standard treatment is better.
- The study is invalid.
- Median.
- Mode.
- Mean.
- Interquartile range.
- Independent samples t-test.
- Paired t-test.
- Chi-square test of independence.
- One-way ANOVA.
- Binomial distribution.
- Poisson distribution.
- Normal distribution, regardless of the population distribution.
- Uniform distribution.
- A smaller sample size will be needed.
- A larger sample size will be needed to detect a statistically significant difference.
- The standard deviation is irrelevant to sample size.
- The study should be abandoned.
- Chi-square test.
- Independent samples t-test.
- Pearson correlation.
- One-way ANOVA.
- Bar chart.
- Pie chart.
- Box plot.
- Line graph.
- Reject the null hypothesis.
- Fail to reject the null hypothesis.
- Accept the alternative hypothesis.
- The result is statistically significant.
