Applied Statistics for OpEx

Summary

This course provides a practical introduction to statistical thinking and data analysis for participants who want to make better, evidence-based decisions.

Many improvement teams collect and report data, but struggle to interpret what it is actually telling them. This can lead to incorrect conclusions, missed insights, and ineffective decision making. This course focuses on how to understand, analyse, and interpret data in a practical context. Participants will learn how to describe variation, identify patterns, and apply statistical methods to support robust decision making.

How will I benefit?

After the course you will be able to:

  • Make confident decisions based on data
  • Understand whether changes in performance are real or simply due to variation
  • Quantify risk and likelihood using probability and statistical thinking
  • Identify relationships between variables & use them to inform decisions
  • Challenge assumptions and unsupported conclusions
  • Communicate data-driven insights clearly and credibly to colleagues and stakeholders

The result is a more structured, evidence-based approach to decision making. You will move beyond simply reporting data to interpreting it, challenging it, and using it to support robust, evidence-based decision making in your organisation.

Who should attend?

This course is ideal for participants who work with data and want to develop stronger analytical capability. It is particularly suited to:

  • Those involved in problem solving or improvement activities
  • Participants progressing from YB or foundation structured problem-solving training
  • Quality and continuous improvement professionals
  • Manufacturing and process engineers
  • Team members responsible for analysing or reporting performance data

Course Outline / Key Topics

Why statistical thinking matters

  • Why data is misinterpreted
  • Common mistakes in data-driven decision making

Data Collection and Sampling

  • Is the data we are using reliable?
  • Are we sampling in a way that represents the process?

Understanding Variation and Distributions

  • Mean, median, mode
  • Standard deviation and spread
  • Interquartile range and box plots
  • Visualisation
  • Normal distributions

Predicting outcomes

  • Probabilities
  • Inferred statistics
  • Introduction to non-normal data (binomial / Poisson)

Normality and Data Behaviour

  • Why normality matters
  • Introduction to normality testing
  • Can we apply standard statistical tools to this data?

Confidence intervals

  • What range is the true performance likely to fall within?
  • How confident are we?

Hypothesis Testing

  • Has something actually changed?
  • Is this difference real or just random variation?
  • F-tests, T-tests, ANOVA

Understanding Relationships in Data

  • Correlation – how strong is the relationship?
  • Regression – can we model the relationship?

Practical Interpretation and Decision Making

  • Based on the data, what decision would you make and why?
  • Reflection on practical application in the workplace