Experimental Design for Peptide Research

Rigorous experimental design is the foundation of reproducible peptide science. This section covers the statistical and methodological principles that ensure experimental validity, minimize bias, and maximize the information obtained from each experiment.

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Statistical Design for Peptide Experiments

Factorial designs, randomization, blocking, and analysis of variance (ANOVA) for peptide research experiments with multiple variables.

→ Full and fractional factorial designs

→ Randomized complete block design

→ Design of Experiments (DoE) approach

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Controls and Validation in Peptide Research

Selection and implementation of positive controls, negative controls, vehicle controls, and method validation for peptide bioassays.

→ Positive control selection criteria

→ Vehicle and sham control design

→ Reference standard qualification

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Dose–Response Experimental Design

Principles of dose selection, curve fitting algorithms, EC₅₀/IC₅₀ determination, and experimental optimization for peptide concentration-response studies.

→ Log-spaced concentration selection

→ Four-parameter logistic (4PL) fitting

→ Hill slope interpretation

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Sample Size and Power Analysis

Statistical power, effect size estimation, sample size calculation, and methods to avoid underpowered peptide experiments.

→ Type I and Type II error control

→ Cohen's d and effect size metrics

→ G*Power and sample size software

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Reproducibility in Peptide Research

Frameworks for ensuring experimental reproducibility — preregistration, blinding, biological vs technical replicates, and data transparency standards.

→ ARRIVE and MIAPE reporting guidelines

→ Blinding and randomization protocols

→ Open data and FAIR principles