Resources
A working library, with a note on what each one actually shows and where it stops. These come out of five verified research passes run in August 2026. Every pass also failed to cover things, and those gaps are recorded alongside the findings rather than smoothed over. Sources marked free can be read without a university login. For the paywalled ones, a public library card often gets you in, and many libraries hand them out online in a few minutes.
Unfamiliar with a term? Jump to the glossary at the foot of this page.
Behind each entry sits a claim-by-claim audit trail with verbatim quotes, so anything I say from these can be traced to a page. If a note above overstates what a source shows, tell me and I will correct it in public.
These five carry most of the weight in a results section.
How much two things move together, on a scale from minus one to one. Zero means no relationship at all. One means perfectly locked together.
To feel how big it is, square it. That gives you roughly the share of the variation it accounts for. An r of .56 is about 31%. An r of .07 is about 0.5%.
The typical distance from the average. If average sleep is seven hours with a standard deviation of one hour, most people land between six and eight.
Small standard deviation means everyone is bunched together. Large means they are spread out.
This is the one behind the argument about the 7.85 ratio, so it is worth a minute.
Individual scores bounce around a lot. Averages of many scores bounce around much less, because averaging cancels out the extremes. How much less? Divide by the square root of how many things you averaged together.
Average 4 things, the spread halves, because the square root of 4 is 2. Average 100 things, the spread shrinks tenfold. Average 78 things and it shrinks by about 8.8.
The probability of seeing a result at least this big if nothing real were going on. A p of .03 means 3%. By convention anything under .05 gets called significant, which is a bad word for it.
Two things it does not tell you, and the second catches nearly everybody. It says nothing about how big the effect is. And it is not the probability that there is nothing there. It only says how surprising your data would look if there were nothing there. A tiny meaningless effect measured in enough people produces a beautiful p-value.
How big, as opposed to whether. The p-value answers is there anything here. The effect size answers is it worth caring about.
They come in several currencies. Correlation is one. So is Cohen’s d, and partial eta-squared, which is the share of variation one factor accounts for.
A confidence interval is a range for how uncertain one estimate is. If it crosses zero, then no effect at all is still on the table.
A credibility interval is a different animal that gets confused with it. When many studies are pooled, it describes how much the true effect varies from setting to setting rather than how uncertain the average is. A wide one means the effect really does differ by context.
N is how many people. Small n sometimes means how many in one group.
k is how many studies, and you only see it when someone is pooling studies together.
The design decides what the study is allowed to conclude. This matters more than the numbers.
One snapshot. Many people, measured once. Cheap, common, and it cannot tell you what caused what or how anything changed.
The same people followed over time. Better for direction, because you can at least see which thing moved first.
Between-person compares different people to each other. Within-person compares one person to themselves at another moment.
They answer different questions and are constantly confused for one another. This confusion is the subject of your whole project.
The researcher decides by chance who gets what. This is the thing that licenses saying one caused the other. Without it you have a pattern, not a cause.
Dividing people into groups afterward, based on what happened to them rather than what you assigned. It produces tables that look identical to a real experiment and proves far less.
Writing down what you predict, and how you will test it, before you collect the data. It stops a researcher quietly changing the question once they see the results.
A preregistered result carries more weight than one that was not. And a finding a paper stumbles into afterward is exploratory, which means suggestive rather than established, even inside a preregistered study.
Someone running it again. Direct means the same design as closely as possible. Conceptual means the same idea tested a different way.
A finding that has never been directly replicated is a finding that has been agreed with, not confirmed.
The study found nothing. This is real information and it is much harder to publish, which means the published record leans toward things having worked.
Pooling many studies into one estimate. Stronger than any single study, and it inherits every flaw in the studies it pools. If they all measured the thing badly, the meta-analysis measures it badly with more decimal places.
A correlation adjusted upward to account for imperfect measurement. Always larger than the raw number.
Not cheating, but it means you cannot compare a corrected number from one paper to an uncorrected one from another.
These are the ways a result can look real and not be.
Something else that could explain the result. The two things you measured both move, and a third thing you did not measure is moving them.
When one person supplies both measurements, their answers agree with each other more than reality warrants. People are consistent about their own opinions.
Something that changes the size of an effect. Not a cause of the outcome. A dial on how strongly the cause works.
Too few people to detect the effect reliably. If it finds nothing, that is not evidence the effect is absent, only that this study could not see it. If it finds something, the size is probably overstated.
Near the end of most papers there is a funding statement and a conflict-of-interest declaration. Read them. They tell you who sponsored the work and whether the authors have a stake in the answer.
Funding does not make a finding false, and independent work can still be wrong. What a sponsor buys is usually subtler than faked results. It shapes which questions get asked, which comparisons get run, and which findings get submitted at all. So the check is not who paid, it is whether the design quietly favors the payer — the comparison chosen, the outcome measured, the follow-up length.
Extremes drift back toward average on their own. Measure people on a terrible day and they will look better next time whatever you do to them.
It is the reason a self-experiment started because things were going badly will tend to show improvement.
Nomothetic means studying what is true of people in general. Idiographic means studying one person in depth.
Almost all of psychology is the first. Your project is arguing for more of the second.
A borrowed term from physics. A process is ergodic if what is true across a group at one moment is also true of each individual over time.
The conditions it requires are strict and human behaviour often fails them. The argument is not that group findings are always wrong about you. It is that you cannot assume they hold for you without checking, and almost nobody checks.
How much of the total variation sits between people rather than within them. An ICC of .7 means 70% of the variation is differences between people and 30% is people differing from themselves.
Pinging people several times a day during ordinary life and asking what is happening right now. It beats asking someone to remember last month.
EMA stands for ecological momentary assessment, which is the same thing in a lab coat.
An experiment with one participant, run properly. Not a diary. It means repeating the switch between conditions several times, ideally without knowing which one you are in, so the pattern cannot be wishful thinking.
The share of the differences among people, in one population at one time, that tracks with genetic differences among them.
It is not the share of you that came from your genes. There is no such quantity. And the figure moves when the environment changes, without any genes changing.
Open access means free and legal. Paywalled means the journal wants money, usually $30 to $50 for one article.
A preprint is the version before peer review, posted free. Useful, and it may differ from the final paper, so check.
A green copy is the author’s own accepted version, posted on a university site. Legal, free, same content, different formatting.
A DOI is a permanent address for a paper, starting with 10-point-something. It is the thing to search when a link dies.
PMC and PMID are identifiers in the free US National Library of Medicine database. A PMC number means a free full text exists.
Unpaywall first, which you already have. Then PubMed Central, then Semantic Scholar, then the author’s own university page. Then a public library card, which gets you into databases from home.
Then email the author. They own the right to send you a copy and most are pleased to be asked.