Estimation and Inference
Refresher reading access
Introduction
In this learning module, we present the various methods for obtaining information on a population (all members of a specified group) through samples (part of the population). The information on a population that we seek usually concerns the value of a parameter, a quantity computed from or used to describe a population of data. In Lesson 1 we introduce sampling, which we use a sample to estimate a parameter; we make use of sample statistics. A statistic is a quantity computed from or used to describe a sample of data.
Supposing that a sample is representative of the underlying population, how can the analyst assess the sampling error in estimating a population parameter? In Lesson 2, the Central Limit Theorem helps us understand the sampling distribution of the sample mean in many of the estimation problems we face. This provides guidance on how closely a sample mean can be expected to match its underlying population mean, allowing an analyst to use the sampling distribution to assess the accuracy of the sample and test hypotheses about the underlying parameter. Lesson 3 covers various resampling approaches.
Learning Outcomes
The candidate should be able to:
- compare and contrast simple random, stratified random, cluster, convenience, and judgmental sampling and their implications for sampling error in an investment problem
- explain the central limit theorem and its importance for the distribution and standard error of the sample mean
- describe the use of resampling (bootstrap, jackknife) to estimate the sampling distribution of a statistic
0.75 PL Credit
If you are a CFA Institute member don’t forget to record Professional Learning (PL) credit from reading this article.