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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:
- Interpret p-values correctly while avoiding common misconceptions
- Distinguish between Type I and Type II errors and explain their consequences for research conclusions
- Define statistical power and explain its implications for thesis design
- Recognise that a non-significant result does not equate to a negative or null finding
Content:
- Probability concepts relevant to research
- Null and alternative hypotheses (brief from previous lesson)
- P-values: meaning and common misconceptions
- Type I and Type II errors
- Statistical power and its implications for thesis validity
- Why a non-significant result is not “negative”