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# 1. Introduction

## a. define simple random sampling and a sampling distribution;

## b. explain sampling error; ## c. distinguish between simple random and stratified random sampling;

What is a simple random sample? A simple random sample is a sample that is obtained in such a way that each element of the population has an equal probability of being selected. The selection of one element has no impact on the chance of another element being selected.

What is a biased sample? A biased sample is one in which the method used to create the sample results in samples that are systematically different from the population.

What is sampling error (also called error of estimation)? Sampling error is the difference between the observed value of a statistic and the quantity it is intended to estimate. For example, sampling error of the mean equals sample mean minus population mean.

What is sampling distribution? - The sampling distribution of a statistic is the distribution of all the distinct possible values that the statistic can assume when computed from samples of the same size randomly drawn from the same population. The most commonly used sample statistics include mean, variance, and standard deviation. - It can also be defined as the relative frequency distribution that would be obtained if all possible samples of a particular sample size were taken.

Why are sampling distributions important? Almost all inferential statistics are based on sampling distributions.

What is stratified random sampling? The population is subdivided into subpopulations (strata) based on one or more classification criteria. Simple random samples are then drawn from each stratum (the sizes of the samples are proportional to the relative size of each stratum in the population). These samples are then pooled.

Why is stratified random sampling used? It is used to ensure that population subdivisions of interest are represented in the sample. Estimates of parameters produced from stratified sampling have greater precision (i.e., smaller variance or dispersion) than estimates obtained from simple random sampling.

What is pure bond indexing? Investors may want to fully duplicate a bond index by owning all the bonds in the index in proportion to their market value weights.

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