Statistical significance is the conclusion that a difference or relationship found in a study is unlikely to be due to chance alone, conventionally decided when the p-value is less than .05, meaning researchers can reject the null hypothesis.
Statistical significance is psychology's way of asking, "Is this result real, or did we just get lucky?" When researchers compare groups (say, a sleep-deprived group versus a well-rested group on a memory test), the scores will almost never be exactly equal. Random variation guarantees some difference. Statistical significance tells you whether the gap is big enough, relative to that random noise, to take seriously.
The decision runs through the p-value. A p-value is the probability of getting results at least this extreme if chance alone were operating (in other words, if the null hypothesis were true). Psychology's conventional cutoff is p < .05, meaning there's less than a 5% chance the result is a fluke. Cross that threshold and the result is called statistically significant, so researchers reject the null hypothesis. One thing to lock in now: significant means "probably not chance." It does not mean "proven true" and it does not mean "big" or "important."
Statistical significance lives in Topic 1.5, Statistical Analysis in Psychology, and it's part of the research-methods toolkit that the rest of the course leans on. Every claim you read in later units, from neurotransmitter effects to therapy outcomes, only counts as evidence because someone tested it for significance first. The revised AP Psych exam makes this concrete with the AAQ (Article Analysis Question), where you read a real study summary and interpret its statistics in writing. If you can't say what p < .05 actually means, the AAQ will expose that fast. It's also a reliable multiple-choice target, usually framed as "a study found a difference with p = .04, what can you conclude?"
Keep studying AP Psychology Unit 1
P-value (Unit 1)
The p-value is the number that does the deciding. Statistical significance is the verdict, and the p-value is the evidence. If p drops below .05, the result gets the significant label.
Null Hypothesis (Unit 1)
Significance testing is really a contest against the null hypothesis, the assumption that there's no real effect. A significant result means the data are too unlikely under "no effect," so you reject the null. You never prove the alternative; you just rule out chance as a good explanation.
Experiments and sleep research (Unit 1)
Significance is what turns an experiment's raw numbers into a conclusion. A classic exam setup uses Unit 1 content, like finding that sleep deprivation lowered cognitive performance with p = .04. Because .04 < .05, the researcher can conclude the drop probably wasn't chance.
Correlation Coefficient (Unit 1)
Significance testing isn't just for experiments. Correlations get tested too, asking whether a relationship like r = .30 could plausibly appear by chance in a sample. A significant correlation still tells you nothing about causation, though. That rule never goes away.
Multiple-choice questions hit this term in two predictable ways. First, the straight definition question: what significance level do psychologists typically use to suggest results aren't due to chance? (Answer: .05.) Second, the interpretation question, where you're given a result like "sleep deprivation reduced cognitive performance, p = .04" and asked what you can conclude. The right answer is always the modest one: the difference is unlikely to be due to chance, so the null hypothesis can be rejected. Wrong answers will tempt you with overclaims like "this proves the hypothesis" or "the effect is large." On the free-response side, the AAQ (like 2025 AAQ Q1, a six-part question requiring complete sentences and appropriate psychological terminology) asks you to interpret the statistics reported in a real study. Being able to state in a sentence what a significant result does and does not mean is exactly the skill that question rewards.
Statistical significance answers "is this result likely real?" Practical significance answers "is this result big enough to matter?" With a huge sample, a tiny, meaningless difference (like 0.2 points on a 100-point test) can still come out statistically significant because even small effects become detectable. The AP exam loves this distinction. A significant result is probably not chance, but you need effect size or context to judge whether it's actually important in the real world.
A result is statistically significant when it's unlikely to have occurred by chance alone, which in psychology conventionally means a p-value less than .05.
The p-value is the probability of getting your results if the null hypothesis (no real effect) were true, so a small p-value lets you reject the null.
Statistical significance does not prove a hypothesis is true; it only means chance is an unlikely explanation for the result.
Statistically significant is not the same as important or large, because big samples can make tiny, trivial differences significant.
On the exam, the safe interpretation of p = .04 is that the observed difference is probably not due to chance, nothing stronger than that.
It's the conclusion that a study's result (a difference between groups or a relationship between variables) is unlikely to be due to chance alone. Psychologists typically use a cutoff of p < .05, meaning less than a 5% probability the result is a fluke.
No. A p-value of .04 means there's only a 4% chance of getting results that extreme if chance alone were at work, so you can reject the null hypothesis. It doesn't prove anything, and there's still a small chance the result is a false positive.
The p-value is the number; statistical significance is the judgment based on that number. If the p-value falls below the .05 threshold, the result is declared statistically significant.
No, this is the classic trap. Significance only means the result probably isn't chance. A tiny effect can be statistically significant if the sample is large enough, which is why researchers also look at effect size to judge practical importance.
Yes. It shows up in multiple-choice questions about the .05 convention and interpreting results like p = .04, and the AAQ free-response question asks you to interpret the statistics from a real research article using correct terminology.