ANALYSING DATA: STATISTICAL TESTS FOR TWO GROUPS

INTRODUCTION

Accurately analyzing and comparing data between two groups is a fundamental aspect of statistical research. Choosing the correct statistical test and properly interpreting results are critical to ensuring valid and reliable conclusions. However, many common errors and misinterpretations can lead to misleading findings, affecting decision-making in research, business, and policy.

This Analysing Data: Statistical Tests for Two Groups session builds on previous descriptive and inferential statistics sessions by applying statistical theory in a practical, hands-on context. Through group exercises and participative learning, participants will learn how to:

✔ Organize and present data effectively
✔ Understand how sample size impacts statistical power and reliability
✔ Formulate statistical hypotheses linked to research questions
✔ Select and apply appropriate statistical tests for two-group comparisons
✔ Identify and avoid common pitfalls and misleading data interpretations

Participants will work with real datasets, exploring how analysis choices influence conclusions and how data can sometimes be manipulated to support misleading claims.

SESSION OBJECTIVES

By the end of this 3-hour session, participants will be able to:

✔ Recognize the importance of data organization and presentation in statistical analysis
✔ Understand how sample size affects statistical significance and reliability
✔ Formulate statistical hypotheses based on research objectives
✔ Select the appropriate statistical test for comparing two groups
✔ Apply statistical tests to real-world datasets
✔ Critically assess results to avoid misleading conclusions and data manipulation

TRAINING METHODOLOGY

This session is highly interactive and practical, using group activities and real-world case studies to reinforce learning. Participants will be encouraged to work collaboratively to analyze data, interpret results, and discuss common statistical errors.

The training will include:
✔ Group exercises applying statistical principles to real datasets
✔ Step-by-step guidance on selecting and applying statistical tests
✔ Discussions on best practices for data presentation and organization
✔ Exploration of common data analysis pitfalls and how to avoid them

By the end of the session, participants will have hands-on experience in statistical testing, strengthening their ability to apply these methods confidently in their work.

WHO SHOULD ATTEND?

This course is designed for professionals and researchers who need to analyze data, compare results between two groups, and interpret statistical findings accurately. It is particularly relevant for:

✔ Researchers & Academics conducting quantitative studies
✔ Business & Data Analysts comparing performance metrics
✔ Healthcare & Epidemiology Professionals analyzing treatment outcomes
✔ Finance, Risk, and Operations Specialists evaluating business metrics
✔ Policy Makers & Public Sector Professionals assessing comparative data

Pre-requisites:

✔ Basic numeracy skills
✔ Completion of "Introduction to Descriptive Statistics" or equivalent understanding
✔ Completion of "Introduction to Inferential Statistics" or equivalent understanding

SESSION CONTENT

1. Organizing and Presenting Data for Statistical Testing

✔ The role of data structure and organization in analysis
✔ Choosing the correct graphical and tabular presentation
✔ Common mistakes in data visualization

2. The Impact of Sample Size on Results

✔ Understanding statistical power and effect size
✔ How small or large samples affect test reliability
✔ Avoiding overgeneralization and misinterpretation

3. Formulating Statistical Hypotheses

✔ Understanding the link between research questions and hypotheses
✔ Defining null and alternative hypotheses
✔ One-tailed vs. two-tailed tests: When to use each approach

4. Selecting and Applying Statistical Tests for Two Groups

✔ Choosing the right test:

• T-tests (Independent and Paired Samples)

• Mann-Whitney U Test (Non-parametric alternative)
  ✔ Interpreting P-values, confidence intervals, and effect sizes
  ✔ Hands-on practice: Applying tests to sample datasets

5. Identifying Pitfalls and Avoiding Misleading Data Analysis

✔ Recognizing common errors in statistical testing
✔ How data can be misrepresented or manipulated
✔ Critical thinking when interpreting statistical findings

DURATION

⏳ 3 Hours

This session provides a practical and insightful introduction to statistical tests for two-group comparisons, equipping participants with the tools to apply, interpret, and critically assess statistical analyses in their work.

CERTIFICATION

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