Table 3 a - e Relative frequency (as %) of medical graduate responses on competencies in statistics and probability that medical schools need to provide

  Graphical presentation of data:

14.2

1.1

  Cross tabulating frequencies or percentages:

28.9

16.1

  Survival analysis:

65.0

7.2

  Forest plots:

51.8

17.5

  Receiver operating characteristic (ROC) curves:

45.4

18.3

  Cluster analysis:

52.7

26.7

  Time series analysis:

36.4

27.6

  Arranging data in spreadsheets for statistical analysis:

14.4

2.2

  Working with subsets of the original data - filtering data:

38.0

15.3

  Merging similar datasets:

38.8

27.5

  Types of response data:

41.5

17.3

  Using Excel for statistics-tips and warnings

23.0

9.3

  Getting to know the fundamentals of a statistical package such as SPSS:

22.6

14.6

  Sensitivity, specificity and positive and negative predictive values:

24.3

0

  Comparing a study cohort with a general population:

56.6

4.4

  Statistical risk estimates:

62.6

6.3

  Simple descriptive (or summary) statistics:

21.7

5.5

  Confidence intervals:

34.5

0.4

  Summarising and analysing missing data:

50.2

20.6

  Laws of probability:

46.4

3.2

  Concepts and rules of probability:

61.1

2.9

  Presenting the findings and conclusions of statistical hypothesis tests:

28.6

5.4

  Statistical significance, statistical power and some facts about p-values:

39.6

0.7

  Valid reporting and interpretation of statistical findings:

48.4

3.6

  Statistical effect sizes:

51.5

2.2

  Principles of good study design:

5.3

1.8

  Sample size calculations:

47.1

8.5

  Randomization:

59.3

0.7

  Misuse of statistics: some statistical blunders and phenomena to look out for in published literature:

52.9

5.1

  Different types of study design:

64.7

0.4

  Statistical aspects of clinical trials:

65.8

2.9

  Retrospective power calculations versus examination of confidence intervals:

48.7

21.5

  Cross-over trials:

70.5

10.3

  Tests of normality:

43.8

8.0

  Simple linear regression analysis:

43.9

11.2

  Hypothesis tests for a single group of continuous data:

35.4

13.0

  One-tailed versus two-tailed hypotheses tests:

48.7

9.2

  Hypothesis tests for categorical data:

35.8

14.0

  Hypothesis tests for comparing two groups of measurement or ordinal data:

31.5

17.4

  Analysis of variance (ANOVA):

45.6

15.2

  Multiple linear regression analysis:

58.3

16.2

  Analysis of covariance (ANCOVA):

42.4

23.0

  Tests of homoscedasticity (or, ‘equality’ of variance):

30.5

30.1

  Correlation coefficients –linear and non-linear:

53.1

10.0

  Statistical indices for measuring levels of agreement and consistency:

55.1

9.9

  Assessing agreement between two methods of measurement:

48.9

16.4

  Understanding the proper meaning of an audit:

16.4

1.1

  Designing survey questions to support valid statistical analyses:

36.2

10.0

  Health-related data sources:

45.2

7.7

  Representing socioeconomic status:

58.6

6.1

  Conducting critical appraisals:

30.3

4.1

  Systematic reviews:

65.5

2.2