The Blog
Tips on writing, editing, and getting the most from our team.
Research Methods
Systematic Literature Reviews: An Overview
A clear introduction to the systematic literature review: what it is, its defining characteristics, and the strengths and weaknesses that place it at the top of the hierarchy of evidence.
Writing Tips
The Oxford Comma: What It Is and When to Use It
What is the Oxford comma, when should you use it, and is it ever truly necessary? We explain the serial comma with clear examples and a few amusing pitfalls.
Writing Tips
Appositives: What They Are and How to Punctuate Them
What is an appositive? We define apposition, work through clear examples, explain when to use commas, and show how appositives connect to the Oxford comma.
Academic Commentaries
Cross-Cultural Communication in Business
What cross-cultural communication means in a business setting, why it is on the rise, the benefits and barriers it brings, and a simple framework for getting it right.
Research Methods
The Research Onion Explained
Saunders, Lewis, and Thornhill's 'Research Onion' is a simple metaphor for designing coherent research. We explain why it is useful and walk through each of its layers.
Research Methods
Research Philosophy Explained
What is a 'research philosophy', in plain English? We define the term, explain the assumptions that distinguish the major philosophies, and show why your choice matters.
Research Methods
An Alternative to the Research Onion
Creswell and Creswell's framework offers a practical alternative to the Research Onion. We review the book briefly and walk through its 'elements of inquiry'.
Academic Commentaries
Primality Tests: Fermat and Miller-Rabin
An expository introduction to two probabilistic primality tests — the Fermat test and the Miller-Rabin test — including the theory each one relies on.
Academic Commentaries
The AKS Primality Test
The Agrawal-Kayal-Saxena (AKS) test is a deterministic, polynomial-time primality test. This post outlines the theorem behind it and the key steps in implementing it.
Data Science
Tips for using Python for data preparation
A practical, example-driven guide to preparing messy data with Python — gather, standardise, validate, and save a clean dataset you can trust, using a real machine-learning research scenario.