Applied Statistics for Research and Evaluation Training Course

This five-day course builds the statistical understanding behind credible research, monitoring and evaluation, whichever software is used. It covers the foundations of statistics and study design, descriptive statistics, sampling and inference, hypothesis testing and the main tests, regression and association, and the correct interpretation and reporting of results. Participants leave able to design sound analysis, choose the right method, and interpret statistics correctly, the understanding that no software can supply.

Software runs the numbers, but it cannot tell you whether they mean what you think they mean. The most common errors in research, monitoring and evaluation are not button pressing mistakes; they are errors of understanding: the wrong test for the question, a sample that cannot support the claim, a significant result misread as an important one. The statistics behind the software is what separates credible evidence from confident nonsense.

This course builds that understanding, independent of any one tool. It covers the foundations of statistics and study design, descriptive statistics, sampling and the logic of inference, hypothesis testing and the main tests, regression and association, and above all the correct interpretation and reporting of results. It is grounded in the realities of research, monitoring and evaluation, and pairs naturally with our software specific courses in SPSS, Stata, R and Python. Participants leave able to design sound quantitative analysis, choose the right method for a question, interpret results correctly, and report them honestly, the understanding that makes whatever software they use actually trustworthy.

• Explain the practical foundations of statistics in research and evaluation
• Distinguish different types of data and understand how measurement affects analysis
• Summarise and interpret data using appropriate descriptive statistics and visualisations
• Understand sampling, sampling error, confidence intervals and the logic of inference
• Apply the principles of hypothesis testing and interpret p-values cautiously and correctly
• Select appropriate inferential techniques for common research and evaluation questions
• Understand the practical meaning of t-tests, ANOVA, chi-square tests, correlation and regression
• Distinguish statistical significance from practical importance and interpret effect more responsibly
• Recognise common threats to valid interpretation, including confounding, bias and poor design
• Read and report statistical findings more clearly, honestly and critically

This course is designed for professionals who need a stronger practical understanding of statistics for research, monitoring and evaluation, including:

• Monitoring and evaluation officers, managers and specialists
• Researchers, research managers and research assistants
• Programme managers and technical leads who use quantitative evidence
• Public health, social science and policy professionals
• Government, NGO and development sector staff involved in research or evaluation
• Data officers and analysts who want a stronger grounding in statistical reasoning
• Postgraduate students and academics working with quantitative studies
• Commissioners and users of research who need to judge findings critically
• Anyone who wants to understand the statistical logic behind credible evidence

The course uses an applied, concept led methodology. Each idea is introduced concisely and built through real examples, guided calculation, and the critical reading of real analyses. The emphasis is on understanding and judgement, with a daily practical session applying it to a
research or evaluation question.
•Expert led sessions and facilitated discussion
•Worked examples and guided calculation
•Critical reading of real analyses
•Research and evaluation case studies
•A daily practical session and a final applied exercise

Organisations whose staff complete this course can expect:

• More credible interpretation and use of research and evaluation findings
• Better design of studies, surveys and analytical plans
• Stronger internal scrutiny of methods, claims and reported results
• Fewer statistical misunderstandings in reporting and decision-making
• Better communication between technical analysts and programme or management teams
• Improved use of quantitative evidence in planning, performance management and policy work
• Reduced risk of poor decisions based on misinterpreted statistics
• A more statistically literate team capable of using evidence more critically and effectively

Participants completing this course will gain:

• Stronger statistical understanding independent of any one software package
• Greater confidence in reading, questioning and using quantitative evidence
• Better judgement in selecting methods and interpreting findings
• A clearer understanding of what data can and cannot support
• Improved ability to identify weak analysis, misleading reporting and over-claimed conclusions
• Greater confidence in engaging with analysts, researchers, consultants and evaluation reports
• A stronger foundation for using statistical software more intelligently
• More credible communication of findings in reports, presentations and decision-making contexts

At Strategic Revenue Africa, our certification goes beyond proof of attendance—it represents practical competence and measurable capability. Upon successful completion of our training programs, participants are awarded a Certificate of Completion from Strategic Revenue Africa, recognizing their ability to apply acquired knowledge in real-world settings. As an organization focused on architecting sustainable revenue and strengthening organizational performance, our certifications signal that participants are equipped with skills that drive results, not just theory.

No advanced mathematics is required. The course is suitable for participants who need to understand and use quantitative evidence but may not have formal statistical training. The course is relevant whether participants analyse data themselves, commission analysis from others, review research outputs, manage M&E systems or need to interpret statistical findings for planning, programme management or policy decisions. No particular software knowledge is required.