KEY STATISTICAL CONCEPTS 1

INTRODUCTION

A solid understanding of key statistical concepts is essential for accurately analyzing data, drawing meaningful conclusions, and making informed decisions. From descriptive statistics to inferential methods, understanding how data is measured, summarized, and interpreted provides a foundation for more advanced analysis.

This Key Statistical Concepts 1 course introduces participants to the fundamental principles of statistics, covering data measurement, summary statistics, data distributions, hypothesis testing, correlation, and statistical confidence. The session will start from first principles, ensuring that participants grasp how data is structured, analyzed, and used to infer broader conclusions about populations.

Through practical examples and guided discussions, participants will explore statistical variability, confidence, and the impact of data presentation.

This course will cover:

✔ Understanding data measurement and classification
✔ Calculating and interpreting summary statistics
✔ Exploring prevalence and incidence in data reporting
✔ Understanding central tendency and dispersion (Mean, Median, Mode, Range, IQR, Standard Deviation)
✔ Introducing the concept of degrees of freedom
✔ Exploring data distributions and the bell-shaped curve (normal distribution)
✔ Investigating effective data presentation methods
✔ Understanding the Central Limit Theorem and its impact on inferential statistics
✔ Exploring sampling, standard error, and confidence limits
✔ Interpreting statistical confidence (P-values and confidence intervals)
✔ Constructing and understanding one-sided and two-sided statistical hypotheses
✔ Introduction to formulating and testing statistical hypotheses
✔ Exploring correlation analysis (Pearson, Kendall’s tau, Spearman) and the relationship between association and causation

COURSE OBJECTIVES

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

✔ Understand the different types of data measurement and their significance
✔ Learn how to calculate and interpret summary statistics
✔ Recognize how data distributions affect statistical analysis
✔ Understand the Central Limit Theorem and its implications
✔ Gain insight into sampling, standard error, and confidence limits
✔ Interpret statistical confidence using P-values and confidence intervals
✔ Develop an understanding of statistical hypothesis testing
✔ Differentiate between one-sided and two-sided hypotheses
✔ Apply correlation techniques (Pearson, Kendall’s tau, Spearman) to assess relationships between variables
✔ Recognize the difference between correlation and causation

TRAINING METHODOLOGY

This session is interactive and discussion-driven, combining theoretical explanations with practical demonstrations. Participants will work through real-world examples to reinforce their understanding of statistical principles and data analysis techniques.

The training will include:
✔ Step-by-step demonstrations of key statistical concepts
✔ Practical exercises on data summarization and hypothesis formulation
✔ Discussions on best practices for data presentation and interpretation
✔ Case studies and real-world examples to illustrate statistical applications

By the end of the session, participants will have a strong statistical foundation, preparing them for more advanced analysis techniques.

WHO SHOULD ATTEND?

This course is designed for professionals who need to understand and apply statistical concepts in their work. It is particularly relevant for:

✔ Researchers & Academics working with quantitative data
✔ Business & Data Analysts needing to interpret statistical results
✔ Healthcare & Epidemiology Professionals analyzing study outcomes
✔ Finance, Risk, and Operations Specialists assessing trends and uncertainty
✔ Quality Control & Process Improvement Teams using statistical methods
✔ Policy Makers & Public Sector Professionals evaluating data-driven policies

Pre-requisites:

✔ Basic numeracy skills
✔ PC skills (keyboard and mouse proficiency)

COURSE OUTLINE

1. Understanding Data Measurement & Classification

✔ Types of data: Categorical vs. Numerical
✔ Recognizing levels of measurement (Nominal, Ordinal, Interval, Ratio)

2. Summary Statistics & Descriptive Analysis

✔ Measures of central tendency: Mean, Median, Mode
✔ Measures of dispersion: Range, Interquartile Range (IQR), Standard Deviation

3. Prevalence & Incidence in Data Analysis

✔ Understanding prevalence and incidence rates
✔ Real-world applications of rate-based statistics

4. Exploring Data Distributions

✔ Introduction to the normal distribution ("bell curve")
✔ Understanding skewness, kurtosis, and data symmetry

5. Methods of Data Presentation

✔ Choosing the right charts and graphs for different data types
✔ Identifying common mistakes in data visualization

6. Introduction to the Central Limit Theorem

✔ Why sample means follow a normal distribution
✔ How the Central Limit Theorem applies to inferential statistics

7. Sampling, Standard Error, and Confidence Limits

✔ The role of sampling in statistical inference
✔ Understanding standard error and its relationship to confidence limits
✔ How larger samples improve statistical confidence

8. Statistical Confidence: P-Values & Confidence Intervals

✔ What do P-values really mean?
✔ Understanding confidence intervals and how they impact decision-making

9. Statistical Hypothesis Testing

✔ Understanding null and alternative hypotheses
✔ One-sided vs. two-sided tests: When to use each approach
✔ Formulating testable research hypotheses

10. Exploring Correlation & Association

✔ Understanding correlation coefficients (Pearson, Kendall’s tau, Spearman)
✔ Identifying relationships between variables
✔ Exploring the difference between correlation and causation

DURATION

⏳ 1 Day

This course provides a comprehensive introduction to key statistical concepts, equipping participants with the foundational knowledge to analyze, interpret, and present data effectively.

CERTIFICATION

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