Hypothesis testing

A hypothesis test compares a claim about a population to sample evidence. The claim being tested is the null hypothesis, $H_0$, and it always contains an equality such as $=$, $\le$, or $\ge$. The alternative hypothesis, $H_a$, is what you suspect might be true instead and uses $<$, $>$, or $\ne$. For a mean, use the parameter $\mu$; for a proportion or percentage, use $p$. The random variable is the specific individual measurement or count you observe, such as one person's commute time or whether one person voted. Type I error means rejecting $H_0$ when it is actually true; Type II error means failing to reject $H_0$ when it is actually false.