Statistics for psychology, behavioural science & neuroscience

From what is a mean to defending a model in a PhD viva.

Ten volumes, 124 days of fifty minutes. Every idea arrives three times: in plain language, as a picture you can move with your own hands, and as the formula an exam will ask you to compute. Built to clear UGC NET and PhD entrance papers on the way past — not as the destination.

Start day 1 → Which test do I need? UGC NET drill New to this? Start with the primer → SPSS · jamovi · R · NVivo → UGC NET Psychology: all ten units → Hand-computation track → See how it teaches
{{ totalDays }}
study days of 45–55 minutes
{{ totalConcepts }}
concepts indexed and searchable
{{ totalHours }}
hours, if you never reread
7
simulators you can break on purpose
{{ donePercent }}
of the course ticked off
How this course teaches

Nothing here is a static picture

This is the central limit theorem, the idea every inferential test stands on. Textbooks assert it. Here you draw the samples yourself: pick a violently non-normal population, set n small, and watch the distribution of the sample mean refuse to care. Then set n = 4 and watch it care very much.

Plain language first
Every day opens with what the procedure is for, in sentences you could say to a colleague. Notation arrives only once the idea is already yours.
Then the moving picture
Simulators for the sampling distribution, α and β, confidence coverage, F as a variance ratio, r and its scatter. You change a number; the consequence moves.
Then the formula, worked
Every formula card glosses each symbol and computes one example with real numbers — the arithmetic an entrance paper puts in front of you with four options.
And the mistake named
Each day ends with the specific error examiners and reviewers look for on that topic. Knowing the trap is most of what separates a pass from a rank.

Two things to open before day 1

The primer gives you the intuition — twelve short sessions, one everyday metaphor each, no algebra. The software reference gives you the clicks, so that “run a Welch t-test” is never the part that stops you.

Volume 0 · three hours
The primer: intuition first
A tasting spoon for sampling, a bathroom scale for sampling error, a smoke alarm for the p-value, a fishing net for the confidence interval. Twelve sessions, fourteen metaphors, every one with the picture and the mistake it prevents.
Reference · always open
How to actually run it
Thirty analyses with the SPSS menu path, the jamovi panel, the line of R, how to read the output and one APA sentence each — plus NVivo for qualitative work and links to every package, five of them free.

The ten volumes

One to three build the reasoning; four to five are the tests that run most published psychology; six to seven are what measurement and latent-variable work demand; eight and nine are where PhD-level work actually lives. Each volume keeps its own progress in this browser.

{{ v.badge }} {{ v.title }} {{ v.blurb }} {{ v.progressLabel }}
Use out of order
{{ r.title }}
{{ r.blurb }}
Exam coverage

Where the syllabus lives in the course

UGC NET Psychology, most university PhD entrance papers and MPhil screenings draw from the same eleven areas. This maps each one onto the days that cover it, so revision can be targeted rather than anxious.

Syllabus areaWhat it asksTaught in
{{ e.unit }} {{ e.what }} {{ e.where }}

How long, honestly

Arithmetic on the real minute estimates in these volumes. Pick a pace; the figures update against whatever you have already ticked.

{{ r.k }}
{{ r.v }}
{{ r.note }}

Six standards this course holds itself to

01
No test without its effect size
Every procedure is taught with the effect size and interval that must accompany it. A bare p-value is treated as an incomplete answer, in an exam and in a journal alike.
02
Compute it once by hand
Entrance papers ask for arithmetic; understanding asks for it too. Each core statistic is worked through once with small numbers before any software appears.
03
Assumptions before results
Each test is introduced with what it assumes, what violating it costs, and what to do instead — ranked by how much the violation actually matters.
04
Real psychology examples
Depression scores, reaction times, item responses, fMRI voxels, EEG windows — not urns and dice. The domain is where the traps live.
05
The exam answer and the honest answer
Where a syllabus still teaches something the field has moved past — Baron & Kenny, alpha as the reliability, Kaiser's rule — you get both, labelled, so you can pass and still be right.
06
Paired with the R course
This volume set teaches the reasoning; R for Behavioural Science teaches the execution. Days cross-reference each other where the same topic appears in both.
At the end

What you will be able to do on day 113

Read
Open any empirical paper in your field and follow the analysis — including mixed models, SEM and Bayes factors — well enough to say where it is weak.
Choose
Take your own design to the correct analysis, justify it against the alternatives, and state the assumptions you checked before you ran it.
Compute
Complete a source table, a chi-square, a correlation, a d and a power calculation on paper under exam conditions, with the df right.
Defend
Answer the viva question behind the analysis: why this model, why this N, what would have falsified you, and what the effect size means in the real world.
Begin with day 1 → Play in the lab first