Research methods and statistics, explained plainly

The things a dissertation actually turns on, written for somebody who has to do the research rather than somebody who already knows the vocabulary. Free to read, no account needed.

Designing your study

A research question you can actually answer

The difference between a topic and a question, and why the question decides everything that follows.

Objectives, and why your results are organised by them

One objective, one analysis, one paragraph of results.

Which study design you are actually running

Observational or interventional; and if observational, which direction time runs in.

Bias and confounding, in plain words

Bias is a fault in how you got your data. Confounding is a third variable explaining your result.

What you measure

Types of variable, and why the type decides the test

Numerical or categorical; and if categorical, ordered or not.

Paired or independent — the question that changes the test

Are your two sets of numbers from the same people, or different people?

How many, and who

How you choose who is in the study

Random, systematic, stratified, convenience — and what each costs you.

Sample size, and the four numbers it needs

Power, alpha, effect size and variability — and where each one comes from.

Choosing and reading a test

How the right statistical test is chosen

Four questions decide it, and none of them require you to know statistics first.

Normality, and what to do when it fails

What the assumption actually means, how it is checked, and why failing it is not a disaster.

What a p-value is, and what it is not

The single most misreported number in postgraduate research.

Effect size — how big, not just whether

Significance tells you something is there. Effect size tells you whether it matters.

Confidence intervals, and why they say more than a p-value

A range of values your data are consistent with.

Writing it up

Describing your data before you test anything

Mean and SD, or median and IQR — and how to tell which.

Writing a statistical result the way a journal expects

Test statistic, degrees of freedom, p-value, effect size — in that order, in a sentence.

Writing a discussion that is not a second results chapter

What your findings mean, how they sit against other work, and what you cannot claim.

These explanations are the same ones inside Chavery Research Companion, where they sit beside your own study — the planner uses them to ask the four questions that decide your test, and checks the assumptions against the data you actually recorded.