Effect Size vs Statistical Significance
Both terms below come up in the same part of the course, and students mix them up. Here is each one defined on its own, side by side, so you can see where they part company.
Effect size
Hypothesis testing
Effect size measures how big a difference is, in units you can interpret, separately from whether that difference is statistically significant.
An effect size can be as plain as the difference between two means or two proportions, or it can be standardized by dividing that difference by a standard deviation. It answers how much, while a p-value only answers whether there is evidence of any difference at all. For example, if one group averages 78 and another averages 75 with a standard deviation of 10, the raw effect is 3 points and the standardized effect is . AP Statistics does not use the phrase effect size; it asks you to report the difference itself with a confidence interval for it, which is the same idea in plain units.
Statistical significance
Hypothesis testing
A result is statistically significant when its p-value is at or below alpha: a result at least this extreme would be unlikely if the null hypothesis were true.
Statistical significance is a statement about surprise under the null hypothesis, not about size or importance. Its biggest trap is sample size: with enough data, a difference far too small to care about still comes out significant. For example, testing with p-hat (the sample proportion) equal to 0.51 gives and no significance when n = 1000, but the identical 1 percentage point gap gives when n = 100,000. Read significance next to practical significance and a confidence interval, which shows how big the effect might actually be.