Textbook Notes (368,432)
Statistics (133)
STAB22H3 (130)
Chapter 12

# Chapter 12.docx

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Department
Statistics
Course
STAB22H3
Professor
Mahinda Samarakoon
Semester
Winter

Description
Stats: Data and Models – Canadian Edition Chapter 12 – Sample Surveys Idea 1: Examine a Part of the Whole - Draw a sample; because examining a complete population is impractical, or impossible, we examine a smaller groups of individuals selected from the population - Sample surveys – designed to ask questions to a small group of people in the hope of learning something about the entire population Bias - Polls or surveys most often fail because they used a sampling method that over- or underrepresented parts of the population – these sampling methods are biased - There is usually no way to fix bias after the sample is drawn - Modern polls select individuals at random to sample to avoid biased results Idea 2: Randomize - Randomization can protect you from factors that you don’t even know are in the data - Randomization protects us from the influences of all features of our population by making sure that, on average, the sample looks like the rest of the population Idea 3: It’s the Sample Size - How big a sample you need depends on what you’re estimating Does a Census Make Sense? - Census – sampling the entire population - It is difficult to complete a census: some individuals are hard and expensive to locate, populations rarely stand still - Impractical, more complex than sampling Populations and Parameters - Population parameter: parameter (key number) used in a model for a population - Any summary found from the data is a statistic, which is used to estimate a population parameter - When statistics from the sample accurately reflect the corresponding parameters, the sample is said to be representative Simple Random Samples - Sample method where each combination of people has an equal chance of being selected - Sampling frame – list of individuals from which the sample is drawn - The easiest way to choose an SRS is to assign a random number of each individual in the sampling frame and select only those numbers that satisfy some rule - Sampling variability – sample-to-sample differences seen when each draw of random numbers from the sampling frame selects different people for the sample (not seen as a problem) Stratified Sampling - Sometimes the population is first sliced into groups, called strata, before the sample is selected - Stratified random sampling: Random sampling is used within each strata and the results from the various strata are then c
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