Just a quick overview of what you'll learn in this chapter - Three unknown population parameters to infer (μ, χ², and p), and two methods for making those inferences: estimation (confidence intervals) and hypothesis testing.
A complete guide to the t-test, and the t-estimate of an unknown population mean. The t statistic is used (instead of z) when the population standard deviation (σ) is not given in the question.
When questions are focused on finding an unknown population variance or standard deviation, then the chi-squared statistic is used. This question and solution includes a hypothesis test and an estimation as well as how to read the chi-squared table.
When the data collected in the sample comes in the form of categories, then you're dealing with nominal data. Hypothesis tests will focus on the (unknown) proportion of observations in the population that fit into a specific category. This is a z-test, but it's formula is different from the z-test of one population mean taught in Chapter 10.
Don't confuse the p-value with the population proportion (p). The p-value is an alternate to the rejection region method in deciding whether to reject the null hypothesis. A larger P value indicates the null is the "better explanation" for your data (and that the pattern you're seeing is by chance), while a smaller p-value indicates that your data rejects the null hypothesis and your data reflects the larger population.
A simple confidence interval estimate of the (unknown) population proportion. Estimations questions are almost always easy to solve - The trick is choosing the correct formula.