The probability of obtaining sample data (or more extreme) if the null hypothesis is true.

A statement of no effect or no difference, which we try to disprove.

A statement that contradicts the null hypothesis, representing what we are trying to find evidence for.

The probability of rejecting the null hypothesis when it is actually true (Type I error).

A range of values, calculated from sample data, that is likely to contain the true population parameter.

Point estimate ± (Critical value * Standard error)

$t = \frac{\bar{x} - \mu_0}{s/\sqrt{n}}$

$z = \frac{\hat{p} - p_0}{\sqrt{\frac{p_0(1-p_0)}{n}}}$

df = n - 1, where n is the number of pairs.

$\frac{statistic - parameter}{standard \ error \ of \ the \ statistic}$

One-sample: Compares a sample mean to a known value. | Two-sample: Compares the means of two independent groups.

Two-sample: Compares means of independent groups. | Matched pairs: Compares means of related groups (e.g., before/after).

T-test: Used when the population standard deviation is unknown, estimate using sample std. dev. | Z-test: Used when the population standard deviation is known.

Significance test: Determines if there is evidence to reject a null hypothesis. | Confidence interval: Estimates a range of plausible values for a population parameter.

Means: Random, Independent, Normal (or large sample size). | Proportions: Random, Independent, Large Counts (np ≥ 10 and n(1-p) ≥ 10).