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Learn how to choose and gather samples to ensure valid answers and minimize bias in data collection. Explore different sample designs and understand the importance of random sampling.
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Pg. 268 - 285 Chapter 5: Producing DataSection 5.1: Designing Samples
Chapter Preview Exploratory data analysis describes what data say by using graphs and numerical summaries. What if we want to ask a large group of individuals questions?
Chapter Preview • To get valid answers, we need to produce our data carefully. • Often we use samples to represent a larger population. Section 5.1 deals with the many ways to choose samples.
Chapter Preview • To get valid answers, we need to produce our data carefully. • Once you have chosen a sample, you have a few ways to gather data.
Chapter Preview • An observational studyobserves individuals and variables of interest, but does not try to influence them. • An experimentdeliberately imposes some treatment on individuals in order to observe their responses.
Chapter Preview • Read Example 5.1 on page 270 of your text.
Confounding • Explanatory variable – attempts to explain the observed outcome (p. 121) • Lurking variable – a variable not among the explanatory variables, but still may influence the interpretation of relationships among those variables. (p. 226) • Response variable - Measures an outcome of a study. (p. 121)
Chapter Preview • Section 5.2 is about designing valid experiments that can be used to determine causation. • Section 5.3 is about using simulation to produce data.
Vocabulary • Population: The entire group of individuals that we want information about. • Sample: The part of the population we actually look at to gather information.
Vocabulary • Census: Attempts to contact every individual in the entire population.
Sample Designs • Voluntary Response Samples • Ex: call-in polls, text your vote, etc. • People choose themselves by responding. • People with strong opinions (especially negative opinions) are more likely to respond. • This leads to bias in your sample.
Sample Designs • Convenience Sampling • Ex: Sitting outside a mall or grocery store • Choosing the individuals that are easiest to reach • Another source of bias: convenience sampling is almost guaranteed not to represent the entire population.
Sample Designs • Bias: The design of a study is biasedif it systematically favors certain outcomes.
Sample Designs • Bias: The design of a study is biasedif it systematically favors certain outcomes. THE SOLUTION! Let chance choose the sample. This is the essential principle of statistical sampling.
Sample Designs • Simplest way: Put the whole population in a hat and draw out a handful of individuals for your sample.
Sample Designs • Practice problems: • #1-4 (p. 273)