STATISTICAL PRINCIPLES IN DATA ANALYSIS

INTRODUCTION

Statistical analysis is essential for interpreting quantitative data, drawing reliable conclusions, and supporting evidence-based decision-making. Whether in research, healthcare, or business analytics, understanding the principles behind data analysis and hypothesis testing is crucial for making informed judgments.

This two-day Statistical Principles in Data Analysis course provides a structured introduction to summary statistics, data distributions, confidence limits, and hypothesis testing. It also introduces participants to a framework for selecting appropriate statistical tests and explores how statistical software packages (such as SPSS) can be used for data analysis.

The course is particularly beneficial for those currently conducting research, planning to undertake research, or working with statistical data. A basic familiarity with research methods and statistical terminology is assumed.

This John Varlow | Training and Consultancy course will cover:

✔ Calculating and interpreting summary statistics
✔ Understanding data distributions, including the normal distribution
✔ Exploring the concept of sampling and standard error
✔ Developing skills in formulating and testing statistical hypotheses
✔ Introducing confidence limits and P-values in statistical inference
✔ Learning a structured framework for selecting the correct statistical test
✔ Gaining an introduction to SPSS (or similar software) for data analysis

COURSE OBJECTIVES

By the end of this two-day course, participants will:

✔ Understand summary statistics and their role in data interpretation
✔ Learn how to calculate and interpret measures of central tendency and dispersion
✔ Recognize data distributions and the significance of the normal distribution
✔ Understand sampling, standard error, and their implications in analysis
✔ Gain insight into statistical hypothesis testing and significance testing
✔ Correctly interpret P-values and confidence intervals
✔ Use a structured approach to choosing the appropriate statistical test
✔ Develop an understanding of how statistical software can be applied in data analysis

TRAINING METHODOLOGY

This course combines conceptual learning with hands-on exercises, ensuring that participants develop both theoretical knowledge and practical data analysis skills.

The training will include:
✔ Step-by-step demonstrations of statistical concepts
✔ Hands-on exercises using real-world datasets
✔ Discussions on best practices for statistical analysis
✔ Guided introduction to SPSS (or similar software) for data handling and analysis

Participants will work through four structured sessions across two days, ensuring a comprehensive yet practical learning experience.

WHO SHOULD ATTEND?

This course is designed for professionals and researchers who need to analyze and interpret statistical data. It is particularly relevant for:

✔ Researchers & Academics conducting quantitative research
✔ Healthcare & Epidemiology Professionals working with clinical and public health data
✔ Business & Data Analysts conducting statistical analysis
✔ Finance, Risk, and Operations Specialists making data-driven decisions
✔ Policy Makers & Public Sector Professionals analyzing statistical reports

Pre-requisites:

✔ Basic numeracy skills
✔ PC skills (keyboard and mouse proficiency)
✔ Familiarity with basic research and statistical terminology

COURSE OUTLINE

DAY 1 – Understanding Data and Statistical Foundations

Session 1: Summary Statistics and Data Measurement

✔ Understanding levels of measurement (Nominal, Ordinal, Interval, Ratio)
✔ Calculating and interpreting measures of central tendency (Mean, Median, Mode)
✔ Exploring measures of dispersion (Range, Interquartile Range, Standard Deviation)

Session 2: Data Distributions and Sampling

✔ Understanding data distributions and their implications in analysis
✔ Introduction to the normal distribution and its significance
✔ Exploring sampling techniques and the concept of standard error

DAY 2 – Inferential Statistics and Data Analysis Techniques

Session 3: Hypothesis Testing and Confidence Intervals

✔ Introduction to statistical hypothesis testing
✔ Understanding confidence intervals and their interpretation
✔ Interpreting P-values and statistical significance

Session 4: Choosing Statistical Tests and Using Software Tools

✔ A structured framework for selecting the correct statistical test
✔ Introduction to SPSS (or similar statistical software) for data analysis
✔ Applying statistical techniques to real-world datasets

DURATION

⏳ 2 Days (Four 3-hour sessions)

This session provides a comprehensive introduction to statistical data analysis, ensuring participants gain both theoretical understanding and practical application skills in quantitative research and decision-making.

CERTIFICATION

Upon successful completion of this course, participants will receive a John Varlow | Training and Consultancy Certificate of Completion.