The article discusses how to test if a sample mean is different from a hypothesized population mean. By comparing the sample mean to the hypothesized mean, researchers can determine if the null hypothesis should be rejected. This is done by calculating a test statistic that shows how many standard errors the sample mean is from the hypothesized mean. Two types of errors can occur in hypothesis testing: Type I error (rejecting a true null hypothesis) and Type II error (failing to reject a false null hypothesis). The most common type of hypothesis test is two-tailed, meaning it looks for differences in both directions from the hypothesized mean.