Question 1: Various publications have found a higher than expected incidence of pvalues immediately below p=0.05 as compared to pvalues immediately above p=0.05. Some possible reasons for this overrepresentation could include: 
Reference:  B. Ginsel, et al., The distribution of probability values in medical abstracts: an observational study., BMC Res Notes, Nov 26, 2015. 
Choice A:  Publication bias. 
Choice B:  Statistical fraud. 
Choice C:  Methodological errors (selective reporting, selective analyses, underpowered analysis). 
Choice D:  A and B. 
Choice E:  All of the above. 
Question 2: Which is the most appropriate definition of a pvalue?

Reference:  R.L. Wasserstein and N.A. Lazar, The ASA’s statement on pvalues, context, process, and purpose, The American Statistician, accepted version published online 3/7/2016. 
Choice A:  A pvalue is the probability that the null hypothesis is true 
Choice B:  A pvalue is the probability under a specified statistical model that a statistic al summary of the would be equal to or more extreme than its observed value. 
Choice C:  A pvalue is the probability that the result was a random coincidence 
Choice D:  A pvalue is the probability the results will not hold up if the experiment is repeated 
Choice E:  All of the above. 
Question 3: Effect sizes are more important than pvalues because: 
Reference:  Source: http://amstat.tandfonline.com/doi/abs/10.1080/00031305.2016.1154108

Choice A:  They are unitless measures. 
Choice B:  They are sample independent. 
Choice C:  They tell you how practically meaningful your results are. 
Choice D:  They are unrelated to the pvalue. 
Choice E:  C and D. 
Question 4: In nonmathematical terms, the problem of performing multiple comparisons without controlling for them is that it: 
Reference:  H. Motulsky, Intuitive Biostatistics: A nonmathematical guide to statistical thinking, Oxford Univ Press, (2014), pp. 183.

Choice A:  Makes it difficult to detect cause from correlation among all of the predictors. 
Choice B:  Makes it less likely to achieve a pvalue <= 0.05 among all of the predictors. 
Choice C:  Across all of the predictors, leads to an increased probability of erroneously finding a difference that is statistically significant when in fact all of the null hypotheses are true. 
Choice D:  Makes it more difficult to compile the analysis due to the number of results presented. 
Choice E:  Is difficult to achieve with currently available statistical software. 
Question 5: The reason censoring tick marks are important for the interpretation of a survival curve is:

Reference:  J. T. Rich, et al., A Practical Guide to Understanding KaplanMeier Curves, Otolaryngol Head Neck Surg 143(3), 2010, 331336. 
Choice A:  They represent the total fraction of subjects lost to followup at a given timepoint. 
Choice B:  They yield important information about the number of subjects remaining in the study (and thus the reliability of the curve) at each timepoint. 
Choice C:  They represent the timepoint when a subject reaches the specified event being analyzed. 
Choice D:  They represent fixed time intervals on the KM curve. 
Question 6: The best approach to missing data is:

Reference:  http://www.valueinhealthjournal.com/article/S10983015(10)606353/references 
Choice A:  Last value carried forward. 
Choice B:  A sensitivity analysis of competing approaches. 
Choice C:  Simple imputation. 
Choice D:  Multiple imputation. 
Choice E:  Complete Cases. 