AN INTRODUCTION TO INFERENTIAL STATISTICS

INTRODUCTION

Inferential statistics allows us to draw conclusions from data, make predictions, and assess relationships between variables. While descriptive statistics summarize data, inferential statistics help us determine whether patterns in sample data are meaningful and can be applied to a broader population.

This Introduction to Inferential Statistics course builds on descriptive statistics concepts, introducing sampling methods, confidence limits, statistical hypothesis testing, and correlation analysis. Participants will explore how data relationships are assessed, how statistical hypotheses are tested, and how to distinguish correlation from causation in real-world datasets.

This John Varlow | Training and Consultancy session will cover:

✔ Understanding sampling and standard error
✔ The relationship between standard error and confidence limits
✔ Formulating and testing statistical hypotheses
✔ Exploring correlations and associations in data
✔ Understanding the difference between correlation and causation
✔ Identifying confounding variables and their impact on statistical interpretation

SESSION OBJECTIVES

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

✔ Understand the importance of sampling and how it relates to population data
✔ Learn how standard error impacts confidence limits in statistical inference
✔ Gain an introduction to hypothesis testing and statistical significance
✔ Differentiate between one-tailed and two-tailed tests
✔ Explore correlation analysis and the relationship between variables
✔ Identify causal relationships vs. misleading associations
✔ Recognize the role of confounding factors in data interpretation

TRAINING METHODOLOGY

This session is interactive and discussion-based, combining conceptual learning with practical examples. Participants will work with real-world data to explore sampling, confidence intervals, hypothesis testing, and correlation analysis.

The training will include:
✔ Practical demonstrations of statistical concepts
✔ Guided discussions on data interpretation
✔ Real-world examples to illustrate inferential techniques
✔ Opportunities for participants to explore and apply concepts

By the end of the session, participants will have a deeper understanding of inferential statistics and how they apply to decision-making and data analysis.

WHO SHOULD ATTEND?

This course is designed for professionals who need to analyze data, interpret statistical results, or make data-driven decisions. It is particularly relevant for:

✔ Researchers & Academics working with sample data
✔ Business & Data Analysts needing to apply inferential techniques
✔ Healthcare & Epidemiology Professionals interpreting study results
✔ Finance, Risk, and Operations Specialists assessing data trends
✔ Policy Makers & Public Sector Professionals using statistical evidence

Pre-requisites:

✔ Basic numeracy skills
✔ Completion of “Introduction to Descriptive Statistics” or equivalent understanding

SESSION CONTENT

1. Sampling & Standard Error

✔ Understanding why sampling is used instead of full population data
✔ The role of sample size and variability in statistical accuracy
✔ Calculating and interpreting standard error

2. Confidence Limits & Statistical Certainty

✔ The concept of confidence intervals
✔ Understanding how confidence limits relate to standard error
✔ Why larger samples improve statistical confidence

3. Hypothesis Testing & Statistical Significance

✔ What is a statistical hypothesis, and why does it matter?
✔ The difference between null and alternative hypotheses
✔ One-tailed vs. two-tailed tests: When to use each
✔ The role of P-values in decision-making

4. Correlation & Association in Data

✔ Understanding correlation coefficients and scatterplots
✔ Identifying relationships between variables
✔ Exploring real-world examples of correlation analysis

5. Correlation vs. Causation: Understanding Confounding Variables

✔ Why correlation does not always imply causation
✔ Identifying confounding factors in research and data analysis
✔ How misleading associations can lead to false conclusions

DURATION

⏳ 3 Hours

This session provides a concise yet comprehensive introduction to inferential statistics, equipping participants with the skills to interpret sample data, assess relationships, and make informed statistical decisions.

CERTIFICATION

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