Session 1, Part D:
Bias in Sampling

In This Part: Population and Sample | Random Sampling

Random sampling is a way to remove bias in sample selection. For example, to pick a random sample of 20 people out of a population of a 1,000, you might put all 1,000 names in a hat, then draw 20 of them. Random sampling attempts to reduce bias in sample selection, since every member of the population has an equal chance of being selected. Note 5

In this Interactive Activity, you will have the opportunity to see if you can personally select a sample that is representative of a particular population.

 Problem D2 Can you think of any circumstances in which it would be difficult or impossible to select a simple random sample?

 You may have noticed that each of the problems you looked at in this session began with a question. Providing answers to questions like these is the goal of statistics. However, the variation present in statistical data makes it difficult to give an exact answer to the question posed. In order to identify any patterns present in the variation, we must analyze our data by organizing and summarizing it. Once this analysis is complete, we can interpret the results to answer our questions. In later sessions, we will look at the analysis and interpretation components in more detail. It's also important to remember that when you conduct a statistical investigation, the question you pose is designed to investigate a group ("the population"). The results of an investigation involving a sample are frequently used to draw conclusions about the entire population. If an attempt is made to include every individual from the population in a sample, then the investigation is called a census.

 Problem D3 Why is a census still considered a sample?

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 Session 1: Index | Notes | Solutions | Video