- Descriptive statistics.
- Inferential statistics.
- Data visualization.
- Data collection.
Author: ETEA MCQS.COM
No category found.
- Independent samples t-test.
- Paired t-test.
- Chi-square test of independence.
- One-way ANOVA.
- Histogram.
- Scatter plot.
- Bar chart.
- Line graph.
- 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.
- 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.
- Median.
- Mode.
- Mean.
- Interquartile range.
- 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.
- Binomial distribution.
- Poisson distribution.
- Normal distribution, regardless of the population distribution.
- Uniform distribution.
- Independent samples t-test.
- Paired t-test.
- Chi-square test of independence.
- One-way ANOVA.
- Chi-square test.
- Independent samples t-test.
- Pearson correlation.
- One-way ANOVA.
- 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.
- Continuous.
- Discrete.
- Nominal.
- Ordinal.
- Reject the null hypothesis.
- Fail to reject the null hypothesis.
- Accept the alternative hypothesis.
- The result is statistically significant.
- Range.
- Interquartile range.
- Standard deviation.
- Median.
- Independent samples t-test.
- Paired t-test.
- Chi-square test of independence.
- One-way ANOVA.
- Linear regression.
- Logistic regression.
- Survival analysis.
- ANOVA.
- To immediately accept the findings.
- To critically evaluate the potential for selection bias and its impact on the validity of the results.
- To ignore the methods and focus only on the p-value.
- To ask the researchers to collect more data.
