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HomeCoursesMonitoring, Evaluation, Research & Statistics Training CoursesApplied Statistics for Research and Evaluation Training Course
Monitoring, Evaluation, Research & Statistics Training CoursesNairobi, Naivasha, Mombasa +10 more
Applied Statistics for Research and Evaluation Training Course
Applied statistics course covering study design, descriptive and inferential statistics, regression, interpretation and reporting for research and evaluation.
5 Days
Duration
Certificate
Included
Instructor-Led
Delivery
Foundation → Intermediate
Level
AS
Applied Statistics for Research and Evaluation Training Course
Starting From
$750
per participant
Flexible Delivery
In-Person, Live Online
Language
English
Dedicated Support
Pre & post training
Course Overview
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.
Introduction
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.
Learning Objectives
By the end of the course, participants will be able to:
- 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
Who Should Attend
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
Training Methodology
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
Organizational Impact
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
Personal Impact
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
Course Outline
- Why statistics matters in evidence-based work
- Populations, samples and variables
- Types of data and levels of measurement
- From research questions to analytical questions
- The shape of data and why it matters
- Designing with analysis in mind
- Common statistical misunderstandings in applied work
Practical session: Frame a research or evaluation question, identify the relevant variables and determine what kind of statistical analysis it would require.
- Why descriptive statistics are indispensable
- Measures of central tendency
- Measures of dispersion and variability
- Frequency tables and proportions
- Cross-tabulation and descriptive comparison
- Visualising data honestly and effectively
- Reading a dataset before analysing it
Practical session: Review a dataset descriptively, identify the most important patterns and summarise its key features for a research or evaluation audience.
- Why sampling matters
- Sampling methods and their implications
- Sampling error and uncertainty
- Confidence intervals and what they mean
- From sample results to population claims
- Sample size, power and practical limitations
- Inference in real-world evaluation and research settings
Practical session: Judge the strength of sample-based evidence, interpret confidence intervals and assess what claims a given sample can reasonably support.
- The logic of hypothesis testing
- P-values, significance and practical interpretation
- The t-test
- Analysis of variance
- Chi-square tests for categorical association
- Correlation and the interpretation of relationships
- Choosing the right test for the question
Practical session: Review a set of research and evaluation questions, choose the appropriate statistical test for each and interpret example results critically.
- Why regression matters in applied analysis
- Simple and multiple regression in practical terms
- Correlation, causation and confounding
- Effect size, significance and substantive importance
- Assumptions, limitations and caution in interpretation
- Reporting statistics honestly and clearly
- Course synthesis and applied evidence review
Practical session: Interpret a set of statistical findings from a research or evaluation scenario and prepare a concise, defensible summary of what the evidence does and does not show.
Certification
Certificate of Completion awarded on successful programme conclusion
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.
Programme Inclusions
- Course materials & workbook
- Certificate of completion
- Post-training support (6 months)
Prerequisites
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.
Schedule & Investment
Upcoming Dates & Fees
LocationScheduleInvestmentLanguage
Accommodation & Transfer
Accommodation and airport transfer are arranged upon request. Contact the Training Officer to reserve.
Payment
Transfer payment to the Strategic Revenue Africa account before the course starts. Send proof of payment to:
training@strategicrevenueafrica.comCourse Fee Includes
- Course tuition & training materials
- Two break refreshments and lunch
- Certificate of completion
- Post-training support (6 months)
Travel, visa, insurance and personal expenses are the participant's responsibility.
Frequently Asked Questions
About Applied Statistics for Research and Evaluation Training Course
No. This course is deliberately software-independent. It focuses on the statistical understanding needed to design, interpret and report quantitative analysis well, regardless of whether the work is later done in SPSS, Stata, R, Python, Excel or by another analyst.
Yes. One of the central aims of the course is to help participants match research and evaluation questions to the most appropriate analytical method and to understand why that choice matters.
Yes. A full section of the course is devoted to sampling, inference, uncertainty and confidence intervals, because these ideas underpin the credibility of claims made from data.
Yes. The course addresses one of the most common weaknesses in applied analysis: the misuse and over-interpretation of statistical significance. Participants learn what p-values do and do not show and how to interpret findings more responsibly.
Very much so. The course is built around the realities of research, M&E, programme evidence, public health studies and policy analysis, making it highly relevant for professionals who work in evidence-based sectors.
Yes. The course is highly valuable for managers, commissioners and users of evidence who need to judge whether a reported analysis is sound, whether conclusions are justified and what the findings actually mean.
Either can work. Taken first, it gives participants the conceptual foundation to use any statistical software more intelligently. Taken after a software course, it deepens understanding and helps correct the common problem of being able to produce output without fully understanding what it means.
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