Biases in Sampling

As we have seen in this chapter, all our estimates are based on the sample selected from the population. It is therefore critical that we choose the samples correctly so that our results are not biased. That said, there are many issues that could come in that would make our samples biased and lower the quality of parameter estimates. Let’s look at some of these:

Appropriate Sample Size

We learned that a larger sample size reduces sampling error. However, it has two problems:

  • When we increase the sample size, we are at the risk of choosing data that doesn’t represent the population correctly. This is especially the case with any time series data (such as returns from a mutual fund), because the population parameters may change over time.
  • A large sample size also increases our cost of sampling.

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