Numbers only become useful once someone knows how to read them — otherwise a spreadsheet full of figures stays exactly that, a spreadsheet. This training course from the Arab British Fellowship Training Academy is built to close that gap, turning raw data into the kind of numerical evidence that holds up when a decision is on the line.

Across a full statistical toolkit — data organization, probability, hypothesis testing, variance analysis and regression — participants build both the theoretical grounding and the hands-on ability to work with Excel and SPSS. The course is designed for the reality of government institutions, private companies, research centers and nonprofits alike: wherever raw data needs to become a trend, a red flag, or a measurable result, this is the skill set behind it.

Who Should Attend?

• Staff in planning and strategic-analysis departments.
• Statisticians and analysts in the public and private sectors.
• Academics and researchers in the social and economic sciences.
• Database and information-systems administrators.

By the end of the course, participants will be able to:

• Apply statistical fundamentals to collecting, interpreting and analyzing data.
• Use the right statistical tools to summarize data and present it visually.
• Apply probability distributions and statistical inference within their analysis.
• Analyze relationships between variables using advanced statistical methods.
• Carry out practical analytical tasks in Excel and SPSS.

Module 1: Why Statistics Matters: Core Concepts of Data

• Differences between qualitative and quantitative data.
• Where data comes from and why the source matters for analysis.
• Types of statistical scales and how each one is used.

Module 2: Summarizing Data: Central Tendency, Dispersion and Visuals

• Mean, median and mode as measures of central tendency.
• Standard deviation, variance and range as measures of dispersion.
• Bar charts, pie charts and box plots for visualizing patterns and trends.

Module 3: Collecting and Organizing Data the Right Way

• Surveys, observations and interviews as data-collection methods.
• Coding and grouping techniques.
• Building frequency-distribution tables.

Module 4: Probability Foundations for Analysts

• The mathematical definition of probability.
• Rules of probability: addition and intersection principles.
• Applying probability to real decision-making.

Module 5: Common Probability Distributions

• The normal distribution and its properties.
• Binomial and Poisson distributions.
• Using statistical tables in practice.

Module 6: Putting Theory into Practice with Excel and SPSS

• Entering and organizing data within the software.
• Running descriptive and inferential analyses.
• Extracting results and generating explanatory reports.

Module 7: Testing Hypotheses With Confidence

• Formulating null and alternative hypotheses.
• Determining significance level and p-value.
• The steps of hypothesis testing and how they lead to a decision.

Module 8: Comparing Groups and Modeling Relationships: ANOVA and Regression

• One-way ANOVA for comparing group means.
• An introduction to simple linear regression.
• Reading results and interpreting coefficients.

Module 9: From Numbers to Decisions: Interpreting Results

• Translating statistical output into practical conclusions.
• Avoiding the most common analysis errors.
• The role statistical analysis plays in improving institutional performance.