Behind every evidence-based health policy — a vaccination target, a hospital staffing plan, an outbreak response — sits a set of numbers that had to be collected, checked and interpreted correctly before anyone could act on them. That practical chain, from raw health data to a defensible decision, is what the Training Course in Applying Statistics to Health Data and Decision-Making from the Arab British Fellowship Training Academy is built to teach.

Participants work through the statistical foundations used across the health field and learn to apply the right method to the right question, using recognised tools and techniques to collect and analyse health data, interpret the results, and translate them into health policy and guidance. The course covers the ground needed for accurate analysis and interpretation across individual healthcare, public health and medical research alike.

Who Should Attend?

• Healthcare professionals.
• Health management and epidemiological surveillance specialists.
• Medical researchers and statisticians.
• Students in public health, medicine or health statistics.
• Health programme managers and health planners.

What You Will Be Able to Do

• Apply the statistical foundations used in analysing health data.
• Collect and analyse health data accurately.
• Use core statistical methods to interpret health data results.
• Apply health statistics tools within scientific research.
• Make data-driven decisions in the health field with confidence.

Module 1: Foundations of Health Statistics and Data Types

• Why statistics matter in public health and medicine, and their role in health decision-making.
• The relationship between health statistics and health policy.
• Qualitative versus quantitative data, and discrete versus continuous data.

Module 2: Collecting and Measuring Health Data

• Methods of collecting data in health studies, including random and stratified sampling.
• The importance of data credibility and accuracy at the point of collection.
• Central tendency measures (mean, median, mode) and dispersion measures (standard deviation, variance, range).

Module 3: Distributions and Probability in Health Data

• The normal distribution and its characteristics in health data.
• Applying statistical distributions in data analysis.
• Probability theory and its use in predicting health outcomes and estimating risk.

Module 4: Descriptive Analysis, Graphs and Health Indicators

• Summarising data using tables and graphs to examine health trends.
• Choosing the right chart type to represent different kinds of data.
• Using indicators such as mortality and incidence rates to evaluate public health status.

Module 5: Statistical Testing: Hypotheses, T-Tests and Chi-Square

• Why statistical tests matter for testing health hypotheses, and choosing the right test for the data.
• Using the T-test to compare samples and analyse differences between health groups.
• Applying and interpreting the Chi-square test to examine relationships between variables.

Module 6: Correlation and Regression Analysis

• Measuring relationships between health variables using correlation, including Pearson's coefficient.
• Using simple regression to interpret the relationship between two variables.
• Applying multiple regression to examine the effect of several variables on a dependent one.

Module 7: Statistical Software: Using, Reporting and Comparing Tools

• Introduction to common statistical software such as SPSS and R.
• Inputting and analysing data, then producing accurate statistical reports with these tools.
• Comparing statistical software options and choosing the right one for the analysis and data at hand.

Module 8: Designing and Analysing Health Studies

• Differences between cross-sectional and longitudinal study designs, and designing studies from secondary data.
• Designing and analysing epidemiological studies, including the impact of environmental and social factors.
• Designing clinical studies and applying health statistics to assess treatment efficacy.

Module 9: Advanced Modelling, Prediction and Big Data

• Applying advanced statistical models to analyse multivariate health data.
• Using predictive techniques to assess health risks and forecast future health trends.
• Handling big data in health research and the challenges that come with it.

Module 10: Ensuring Data Quality and Communicating Results

• Identifying and minimising different types of statistical error, including sampling and computational mistakes.
• Testing data validity and the accuracy and reproducibility of results before relying on them.
• Writing clear statistical reports and presenting findings to medical or general audiences using graphs and tables.