Section 7.1: Point Estimation — General Concepts and Criteria

1. Compare and contrast descriptive and inferential statistics in terms of populations/samples and parameters / statistics.
2. Explain why you can never answer a question about a population directly from a sample without making an inference.
3. State the three main types of inferential procedures we will consider in this course, and describe them in plain English.
4. Define point estimator, and state what a point estimator is an estimator for.
5. Distinguish between a point estimator (the procedure) and a point estimate (an application of the procedure to a particular sample).
6. State common point estimators for population parameters, including point estimators for population means, variances, and success probabilities.
7. Given a collection of point estimators and their expected values and variances, come up with a rough ranking of how “good” the point estimators are.
8. Recognize the statistical notation of using \(\theta\) for a generic parameter of a population, and \(\widehat{\theta}\) for a point estimator of that parameter.
9. Define the standard error of a point estimator, and compute the standard error of a point estimator given its sampling distribution.