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Module 1: Foundations of Research and Thesis Development
Upon completion of this module, residents will be able to:
• Understand the thesis requirements and academic standards in basic and paraclinical sciences.
• Identify feasible research topics aligned with departmental resources and expertise.
• Formulate clear, testable research questions using established frameworks.
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Module 2: Advanced Literature Review and Research Proposal Writing
This module builds the conceptual, regulatory, and methodological foundation required to design a sound postgraduate thesis- from understanding institutional requirements to translating ideas into structured research questions, hypotheses, and objectives.
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Module 3: Study Design, Methodology, and Data Management
This module builds the conceptual, regulatory, and methodological foundation required to design a sound postgraduate thesis- from understanding institutional requirements to translating ideas into structured research questions, hypotheses, and objectives.
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Module 4: Comprehensive Biostatistics for Thesis Research
To equip learners with the statistical thinking, analytical skills, and practical tools required to analyze thesis data correctly, interpret results responsibly, and present findings in a defensible, publication-ready format.
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Research Methodology and Biostatistics for Postgraduate Residents in Basic Sciences and Paraclinical Sciences
Learning Objectives:
By the end of this lesson, students will be able to:
- Select the appropriate statistical test based on study design, outcome variable type, and distributional assumptions
- Summarise demographic and baseline characteristics using appropriate descriptive statistics
- Differentiate between statistical significance and clinical or practical significance
- Calculate and report effect sizes alongside p-values
- Avoid common test-selection errors in postgraduate thesis analysis
Content:
- Mapping statistical tests to: Study design, Outcome variables, and Distribution assumptions
- Descriptive statistics for: Demographic data and Baseline characteristics
- Selecting tests based on: Data type, Normality, Sample size
- Primary vs secondary analyses
- Effect sizes and confidence intervals
- Statistical significance vs practical/clinical significance
- Common test-selection errors in postgraduate theses
- Why effect size matters more than p-value alone