STATISTICS AND DATA INSIGHT

INTRODUCTION

In an era where data influences decision-making across industries, understanding how to interpret, evaluate, and critically assess statistical information is essential. Whether data is used in research, business, or media, it is often misrepresented or misunderstood, leading to misleading conclusions and poor decision-making.

This Statistics and Data Insight course provides a content-rich, discussion-based exploration of statistical principles, focusing on how to assess the validity of research findings. The session covers how data is measured, summarized, presented, and analyzed, emphasizing the importance of data distributions, statistical tests, and interpretation of results.

Participants will be provided with a research paper prior to the session, which they will analyze and interpret throughout the day. The course will bridge the gap between statistical theory and real-world application, enabling participants to confidently assess study outcomes and identify common statistical errors.

This John Varlow | Training and Consultancy course will cover:

✔ Assessing the statistical validity of a research paper
✔ Understanding how data is used, misrepresented, and misinterpreted
✔ Recognizing different measurement scales (Nominal, Ordinal, Interval)
✔ Applying measures of central tendency (Mean, Median, Mode)
✔ Understanding measures of dispersion (Range, IQR, Standard Deviation)
✔ Analyzing percentages, rates, and ratios (Prevalence and Incidence)
✔ Evaluating data distributions and the importance of normality
✔ Selecting the correct chart type based on data classification
✔ Differentiating between Independent Measures and Repeated Measures study designs
✔ Identifying whether the correct statistical test has been used for an outcome
✔ Interpreting P-values and Confidence Intervals in hypothesis testing
✔ Understanding why study results alone are not always the most important statistic
✔ Exploring the advantages of data stratification and standardization
✔ Examining time series data, run charts, and forecasting methods
✔ Exploring correlation vs. causation using scatterplots and regression techniques

TRAINING METHODOLOGY

This session differs from traditional statistics courses as it is content-intensive rather than exercise-driven. Instead of hands-on group activities, the course is structured around discussion, critical analysis, and case-based learning.

Participants will receive a research paper before the session, which they will be asked to review in preparation for discussions. The course will be divided into morning and afternoon sessions, each focusing on different aspects of statistical interpretation.

✔ Morning session: Introduction to data measurement, summarization, presentation, and study designs
✔ Afternoon session: Application of statistical tests, interpretation of results, and advanced data analysis techniques

This fast-paced, insight-driven approach ensures that participants gain a broad yet practical understanding of statistical analysis in a short timeframe.

COURSE OUTLINE

1. Evaluating Statistical Validity in Research

✔ Reviewing a research paper – key elements to assess
✔ Common pitfalls and misrepresentations in data reporting
✔ How data misuse affects decision-making

2. Understanding Measurement Scales & Data Classification

✔ The three main types of data: Nominal, Ordinal, Interval
✔ Why levels of measurement matter in statistical analysis

3. Descriptive Statistics & Data Summarization

✔ Measures of central tendency (Mean, Median, Mode)
✔ Measures of dispersion (Range, Interquartile Range, Standard Deviation)
✔ Understanding percentages, rates, and ratios (Prevalence vs. Incidence)

4. Data Distributions & Normality

✔ Understanding the normal distribution and its impact on statistical testing
✔ Identifying skewness, outliers, and bimodal distributions

5. Presenting Data Effectively

✔ Matching chart types to data types for accurate representation
✔ Common errors in data visualization and how to avoid them

6. Study Designs: Independent vs. Repeated Measures

✔ Comparing two-group study designs
✔ Strengths and limitations of independent vs. repeated measures

7. Selecting the Right Statistical Test

✔ How to determine if the correct test has been used in a study
✔ Common tests for comparing two groups

8. Interpreting Statistical Results

✔ Understanding P-values and Confidence Intervals
✔ The role of chance and uncertainty in statistical testing

9. Beyond the Study Result – The Importance of Context

✔ Why the study result itself isn’t always the most important statistic
✔ The role of statistical power and effect size

10. Advanced Data Interpretation Techniques

✔ Understanding data stratification – when and why to use it
✔ Standardization of data – direct vs. indirect standardization

11. Time Series Analysis & Forecasting

✔ The advantages of run charts, moving averages, and smoothing methods
✔ Understanding simple forecasting techniques

12. Correlation vs. Causation

✔ Using scatterplots and trend lines to assess relationships
✔ Why correlation does not imply causation

CERTIFICATION

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