KEY STATISTICAL CONCEPTS 2

INTRODUCTION

Building on the foundational knowledge from Key Statistical Concepts 1, this course delves deeper into statistical hypothesis testing, analytical design, and statistical power. Understanding the differences between independent and related samples, selecting the appropriate statistical test, and ensuring data quality are essential for conducting robust and meaningful analysis.

This session explores statistical tests for comparing two or more groups, covering both parametric and non-parametric methods. The importance of statistical hypotheses, significance levels, homogeneity of variance, and analytical design will be emphasized.

Participants will gain hands-on experience in formulating hypotheses, selecting appropriate tests, and interpreting statistical results to ensure accurate, reliable, and meaningful data analysis.

This John Varlow | Training and Consultancy course will cover:

✔ Formulating analytical questions and hypotheses
✔ Data considerations and quality assurance in statistical analysis
✔ Understanding analytical design: independent vs. related samples
✔ The concept of statistical power and its impact on analysis
✔ Tests for two independent groups (Chi-squared, Independent t-test, Wilcoxon Signed-Rank Test)
✔ Tests for two related groups (McNemar, Paired t-test, Mann-Whitney U Test)
✔ Understanding homogeneity of variance and its importance
✔ Tests for multiple groups (One-Way and Two-Way ANOVA, Kruskal-Wallis)
✔ Understanding one-tailed vs. two-tailed significance in hypothesis testing

COURSE OBJECTIVES

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

✔ Develop the ability to formulate analytical questions and corresponding hypotheses
✔ Understand the importance of data quality and its impact on results
✔ Differentiate between independent and related samples in statistical analysis
✔ Gain insight into statistical power and how it affects hypothesis testing
✔ Select and apply appropriate statistical tests for two-group comparisons
✔ Recognize and address issues of homogeneity of variance
✔ Apply tests for multiple group comparisons and interpret their results
✔ Distinguish between one-tailed and two-tailed significance in hypothesis testing

TRAINING METHODOLOGY

This session is interactive and application-driven, combining theoretical explanations with hands-on exercises. Participants will apply statistical concepts to real-world examples and practice using appropriate tests for different analytical designs.

The training will include:
✔ Step-by-step demonstrations of statistical tests
✔ Hands-on exercises applying tests to sample datasets
✔ Discussions on analytical design and test selection
✔ Guided interpretation of statistical results

By the end of the session, participants will have practical experience in hypothesis testing and statistical analysis, preparing them for more complex data analysis challenges.

WHO SHOULD ATTEND?

This course is designed for professionals who need to apply and interpret statistical tests in research, business analysis, or decision-making. It is particularly relevant for:

✔ Researchers & Academics conducting quantitative studies
✔ Business & Data Analysts performing comparative analyses
✔ Healthcare & Epidemiology Professionals analyzing treatment effects
✔ Finance, Risk, and Operations Specialists evaluating group differences
✔ Policy Makers & Public Sector Professionals assessing comparative data

Pre-requisites:

✔ Basic numeracy skills
✔ PC skills (keyboard and mouse proficiency)
✔ Completion of "Key Statistical Concepts 1" or equivalent understanding

COURSE OUTLINE

1. Formulating Analytical Questions & Hypotheses

✔ Developing research questions and statistical hypotheses
✔ Understanding null and alternative hypotheses
✔ The importance of one-tailed vs. two-tailed significance

2. Data Considerations & Quality Assurance

✔ Ensuring data accuracy, completeness, and reliability
✔ Identifying data errors and inconsistencies

3. Analytical Design: Independent vs. Related Samples

✔ Defining independent samples vs. repeated measures
✔ Choosing the appropriate study design for hypothesis testing

4. Understanding Statistical Power

✔ The role of sample size, effect size, and significance level
✔ How to ensure adequate power in hypothesis testing

5. Statistical Tests for Two Independent Groups

✔ Chi-Squared Test – for categorical data comparison
✔ Independent Samples t-Test – for comparing two means
✔ Wilcoxon Signed-Rank Test – non-parametric alternative

6. Statistical Tests for Two Related Groups

✔ McNemar Test – for paired categorical data
✔ Paired Samples t-Test – for comparing repeated measures
✔ Mann-Whitney U Test – for non-parametric comparisons

7. Homogeneity of Variance

✔ Understanding why variance equality matters
✔ How to test for homogeneity of variance before analysis

8. Tests for Multiple Groups

✔ One-Way ANOVA – comparing more than two groups
✔ Two-Way ANOVA – analyzing interactions between factors
✔ Kruskal-Wallis Test – non-parametric alternative for multiple groups

DURATION

⏳ 1 Day

This session provides a comprehensive introduction to hypothesis testing, analytical design, and statistical power, equipping participants with the skills to apply and interpret statistical tests confidently.

CERTIFICATION

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