1. Overview and general principles of statistics: A brief historical background of how application of statistical principles in medicine was a turning point in the evolution of modern evidence–based medicine will be given. We will discuss how statistics has profoundly affected our decision making and why having statistical skill is absolutely necessary for researchers. This will be followed by a problem–solving based discussion of the concepts of primary and secondary analysis, cluster analysis, P value and type I and II (false positive and false negative) errors.
2. Variable: Definition and different types of variables (continuous, dichotomous, categorical, ordinal, ranked, etc) will be discussed.
3. Expression of data: Expression of data with frequency and summary statistics (mean, median, mode, standard deviation, range, interquartile range, etc) will be discussed.
4. Distribution of data: Normal (Gaussian) distribution of data, measures/tests of normality (Q–Q plot, skewness and kurtosis, Shapiro–Wilks test, one–sample Kolmogorov–Smirnov test, Kolmogorov–Smirnov test with Lilliefor′s significance correction, etc.) and different methods of data transformation (normalization) will be covered.
5. Statistical software: General features of available statistical softwares (Statistical Package for the Social Sciences (SPSS), SigmaPlot, Statistical Analysis System (SAS), Microsoft® Excel, etc.) will be discussed.
6. Missing value analysis: The nature of missing data (missing completely at random (MCAR), missing at random and not missing at random), Little′s MCAR test, methods of handling missing values and imputation will be discussed.
7. Comparison of groups (comparison two and more than two groups; paired versus unpaired data): Unpaired and paired t–tests, analysis of variance (ANOVA), repeated measures ANOVA, general linear model, Mann–Whitney U test, Wilcoxon rank sum test, Friedman two–way analysis of variance, Kruskal–Wallis one–way analysis of variance, post–hoc analysis and adjustment for multiple comparisons will be discussed.
8. Bivariate correlation analysis: Pearson Chi–square, contingency coefficient, Fisher′s exact test, Yate′s continuity correction, Phi and Cramer′s V tests, Pearson′s product moment correlation, Kendall′s tau and Spearman rank correlation tests will be discussed.
9. Multivariate linear regression analysis: Beta coefficient, partial correlation coefficient, assumptions of multivariate linear regression, collinearity statistics and diagnostics (tolerance, variance inflation factor, eigenvalue and condition index) will be discussed.
10. Logistic regression analysis: Hosmer–Lemeshow goodness–of–fit test, binary and multinomial logistic regression will be discussed.
11. Receiver operating characteristic (ROC) curve analysis: A popular graphical method for calculating sensitivity and specificity of different cutoff values for a laboratory or diagnostic test will be introduced.
12. Meta–analysis: Systematic review and meta–analysis statistical softwares(e.g., Cochrane Review Manager) will be introduced and methods of meta–analysis, test for heterogeneity (Q statistic and I2 index), fixed and random effects model, Mantel–Haenszel summary odd ratio, forest and funnel plots will be discussed.
13. Guidelines for reporting the results: Useful hints for presenting data, logically describing the study findings and avoiding common pitfalls of result reporting will be discussed.