Resources
Guides on Design of Experiments for biologists and bioprocess engineers.
Free Design of Experiments Template (Worksheet + Run Tables)
A free design of experiments template: a planning worksheet and ready-to-use run tables for 3- and 4-factor experiments, plus how to fill them in.
Design of Experiments Software for Biologists: BioDOE vs. JMP vs. Minitab
Comparing design of experiments software for bench scientists: BioDOE vs. JMP and Minitab on price, learning curve, and when each actually fits your lab.
Design of Experiments for Biologists & Bioprocess Engineers: The Complete Guide
A complete guide to Design of Experiments for biologists and bioprocess engineers — screening, optimization, hard-to-change factors, blocking, and reading your results.
Response Surface Methodology, Explained
How Response Surface Methodology and central composite designs find the exact optimum across your factors, and why BioDOE uses a face-centered design that never asks you to test outside your tested range.
Plackett-Burman Designs, Explained
What a Plackett-Burman design is, why it screens many factors in very few runs, and why that efficiency comes with messier aliasing than a Resolution IV fractional factorial.
Fractional Factorial Designs and Aliasing, Explained
How fractional factorial designs cut a full factorial down to a fraction of the runs, what aliasing actually means, and what Resolution tells you about the trade-off.
Full Factorial Designs, Explained
What a full factorial design is, why it has zero aliasing, and why run count usually forces you to a fractional factorial once you have more than a few factors.
DOE for Analytical Method Validation (ICH Q2)
How Design of Experiments supports ICH Q2 method validation — testing robustness efficiently by varying method parameters together instead of one at a time.
DOE and ICH Q8: Building the Design Space
How Design of Experiments supports ICH Q8 Quality by Design — mapping Critical Process Parameters to Critical Quality Attributes and defining a Design Space.
DOE vs. OFAT: Why Changing One Variable at a Time Wastes Runs
Why One-Factor-at-a-Time (OFAT) experimentation wastes runs and misses interactions, and how Design of Experiments finds the same answers in fewer runs by changing several variables together.
Definitive Screening Designs (DSD), Explained
What a Definitive Screening Design is, how it detects curvature at a run count close to a standard screen, and when BioDOE uses one automatically.
Which DOE Design Should I Use? A Decision Guide
A plain-language decision guide to choosing between screening, characterization, and optimization designs in DOE, and how factor count and lab constraints change the answer.
Screening Design vs. Response Surface: Which Do You Need?
The difference between a screening design and a response surface (optimization) design in DOE — and how to know which stage your experiment is at.
How Many Runs Do You Actually Need for a Design of Experiments?
How to calculate the right sample size for a Design of Experiments — using assay noise and the smallest improvement worth detecting, without a statistics background.
Using DOE to Optimize ELISA and Immunoassay Development
How Design of Experiments speeds up ELISA and immunoassay development — screening antibody coating, detection conjugate, and blocking agent variables for signal-to-noise.
Designing a DOE for CHO Cell Culture Media Optimization
How to design a Design of Experiments for CHO cell culture media optimization — choosing variables, handling hard-to-change factors, and balancing titer against viability.
Getting Started with BioDOE: A Complete Walkthrough
A step-by-step guide to running your first Design of Experiments in BioDOE, from defining factors through analysis and optimization, with real screenshots.
Hard-to-Change Factors and Split-Plot Designs, Explained
What Hard-to-Change factors are, when to flag one, and how split-plot designs and REML analysis account for them correctly.
How to Block an Experiment After You've Already Designed It
Why blocking usually has to be planned upfront, and how to split an already-generated design into balanced blocks by analyst, day, or batch instead.
Reading Your ANOVA Table and Pareto Chart
A practical guide to interpreting DOE analysis results — p-values, the Pareto chart, Actual vs. Predicted, and curvature — using a real example.