1. Assess sources of bias and variation in published studies and threats to study validity (bias) including problems with sampling, recruitment, randomization, and comparability of study groups | Sampling; blinding and bias; confounding | |

2. Propose study designs for addressing a clinical or translational research question | Study design | |

3. Describe the basic principles and practical importance of probability, random variation, systematic error, sampling error, measurement error, commonly used statistical probability distributions, hypothesis testing, type I and type II errors, and confidence limits | Probability and probability distributions; central limit theorem and variability of statistics confidence intervals; significance testing and *p*-values; clinical relevance versus statistical significance | Statistical errors |

4. Compute sample size, power, and precision for comparisons of two independent samples with respect to continuous and binary outcomes | Power and sample size | |

5. Explain the uses, importance, and limitations of early stopping rules in clinical trials | | Explain the uses, importance, and limitations of early stopping rules in clinical trials |

6. Describe the concepts and implications of reliability and validity of study measurements and evaluate the reliability and validity of measures | Diagnostic testing | Describe the concepts and implications of reliability and validity of study measurements; evaluate reliability measures; evaluate measures of validity for survey data |

7. Scrutinize the assumptions behind different statistical methods and their corresponding limitations and describe preferred methodological alternatives to commonly used statistical methods when assumptions are not met | Assess assumptions and select an appropriate method | |

8. Distinguish among the different measurement scales and the implications for selection of statistical methods to be used on the basis of these distinctions | Variable types; assess assumptions and select an appropriate method | |

9. Generate simple descriptive and inferential statistics that fit the study design chosen and answer research question | Graphing and summary statistics; assess assumptions and select an appropriate method; unadjusted methods for independent continuous data; unadjusted methods for independent binary data; unadjusted methods for independent time-to-event data | |

10. Describe the uses of meta-analytic methods | | Describe the uses of meta-analytic methods |

11. Communicate research findings for scientific and lay audiences | Describe the size of the effect or association; clinical relevance versus statistical significance; confidence intervals; significance testing and *p*-values; statistical significance versus sample size; confounding; blinding and bias | |

12. Describe size of the effect with a measure of precision | Describe the size of the effect or association; Confidence intervals | |

13. Describe the study sample, including sampling methods, the amount and type of missing data, and the implications for generalizability | Sampling; Blinding and bias; graphing and summary statistics | Describe the type of missing data and the implications for generalizability |

14. Interpret results in light of multiple comparisons | Multiple comparisons | |

15. Identify inferential methods appropriate for clustered, matched, paired, or longitudinal studies | | Identify inferential methods appropriate for clustered, matched, paired, or longitudinal studies |

16. Identify adjusted inferential methods appropriate for the study design, including examination of interaction | Assess assumptions and select an appropriate method; adjusted methods for continuous data; adjusted methods for binary data; adjusted methods for time-to-event data | Identify methods to assess interaction |

17. Describe statistical methods appropriate to address loss to follow-up | Unadjusted methods for independent time-to-event data; adjusted methods for time-to-event data | Describe methods to address missing data |