- Statistic.
- Sample.
- Parameter.
- Variable.
No category found.
- Range.
- Standard deviation.
- Interquartile range (IQR).
- Variance.
- The original study had too much power.
- The original study might have had a Type I error, or the replication study a Type II error, or differences in methodology.
- The original study was perfect.
- Replication studies are never necessary.
- Linear regression.
- Logistic regression.
- Poisson regression.
- Cox regression.
- Law of Large Numbers.
- Bayes' Theorem.
- Central Limit Theorem.
- Chebyshev's Theorem.
- Nominal.
- Ordinal.
- Interval.
- Ratio.
- Independent samples t-test.
- Paired t-test.
- One-sample t-test.
- Two-sample z-test.
- Statistics.
- Mathematics.
- Biostatistics.
- Epidemiology.
- To immediately declare the drug unsafe.
- To rapidly analyze the data for patterns and statistical significance, to inform immediate public health action.
- To wait for more data to accumulate.
- To disregard the reports as anecdotal.
- Linear regression.
- Logistic regression.
- Poisson regression.
- Survival regression.
- Independent samples t-test.
- Paired t-test.
- Chi-square test of independence.
- One-way ANOVA.
- P-value.
- Correlation coefficient.
- T-statistic.
- Chi-square statistic.
- Mean.
- Median.
- Mode.
- Standard deviation.
- The drug is highly effective.
- The finding is statistically significant, but the effect estimate is imprecise and less reliable, warranting caution.
- The study had high precision.
- The study is invalid.
- Independent samples t-test.
- Paired t-test.
- Chi-square test.
- One-way ANOVA.
- Standard deviation.
- Variance.
- Range.
- Interquartile range.
- Mean.
- Median.
- Standard deviation.
- Mode.
- Logistic regression.
- Linear regression.
- Poisson regression.
- Survival regression.
