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Example of a Fractional Factorial Design

A step-by-step walkthrough of a fractional factorial design: why, when, and how to use it, with a practical example and visualizations.

Introducing Fractional & Central Composite Designs

When to move past full factorial design; how to use fractional designs to screen and central composite designs to optimize.

Create a Full Factorial Design in Python

Step-by-step guide for beginners: install pyDOE3, create 2^k designs, add factor names, map to real units, randomize, and export ready-to-run experiment plans.

A step by step example of a full factorial design

Learn what a full factorial design looks like in practice and how to create and analyze one—using a real filtration-rate dataset. We’ll read main-effects and interaction plots without heavy stats.

The Basics of Designing Better Experiments

Discover why one-factor-at-a-time (OFAT) approaches miss crucial interactions and how systematic design space coverage leads to full factorial designs. Learn to plan experiments that capture maximum information with minimal runs.

Main Effects & Interaction Effects Explained

Understand main effects and interaction effects in factorial design. Learn how factors can work independently or synergistically in Design of Experiments through practical examples and visualization techniques.

Principles of DoE: Randomization, Replication, Blocking

Learn the three fundamental principles of Design of Experiments that ensure high-quality, reproducible data: replication, randomization, and blocking. Essential DoE basics for reliable experimental results.

Key DoE Terms

Your essential cheat sheet for Design of Experiments terminology. Learn the key terms like factors, factor levels, response variables, and more to master DoE conversations and concepts.

What is Design of Experiments? (DoE Basics)

Learn the fundamentals of Design of Experiments (DoE) - a structured approach to experimental planning that maximizes information from minimal trials. Perfect introduction for chemical process optimization and experimental design.