ECO220Y1 Chapter Notes - Chapter 3: Statistical Parameter, Sampling Frame, Sample Size Determination
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Ch. 3: Surveys and Sampling
LO 3.1 Three Principles of Sampling
Principle 1: Examine a part of the whole
• First step is to draw a sample
Population: the entire group of individuals or instances about whom we hope to learn
Sample: a subset of a population, examined in hopes of learning about the population
Sample survey: a study that asks questions of a sample drawn form some population in hopes of
learning something about the entire population
• Sampling methods that over- or underemphasize some characteristics of the population are
said to be biased
Biased: any systematic failure of a sampling method to represent its population
• Conclusions based on biased samples are inherently flawed
• There is no way to fix bias after the sample is drawn and no way to salvage useful
information from it
• Best strategy to minimize bias is to select individuals randomly for the sample
Principle 2: Randomize
• Randomization can protect against factors that you aren't aware of, as well as those you
know are in the data
• Randomizing protects us from the influences of all the features of our population by
making sure that on average, the sample looks like the rest of the population
Randomization: a defence against bias in the sample selection process, in which each individual
is given a fair, random chance of selection
Two things make randomization fair:
1. Nobody can guess the outcome before it happens
2. Some underlying set of outcomes will be equally likely
Why not match the sample to the population?
• We can't possibly think of all the relevant variables that might be important
Sampling variability: the natural tendency of randomly drawn samples to differ from one
another
Principle 3: The sample size is what matters
• The size of the sample determines what we can conclude from the data regardless of the
size of the population
o The size of the population does not matter at all
Sample size: the number of individuals in a sample, usually denoted by n
• Balance between how well the survey can measure the population and how much the
survey costs
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• Sample needs to be large enough to be representative of the population
LO 3.2 A Census - Does it Make Sense?
Census: an attempt to collect data on the entire population of interest
• Can be difficult to complete a census
o Some individuals are hard to locate or hard to measure
o The population may change
o Can be cumbersome. Requires a large team of people
• A sample surveyed in a shorter time frame may generate more accurate information
LO 3.3 Populations and Parameters
Statistic: obtained from a sample and used to estimate a population parameter
Parameter: unknown values. Have to settle for estimates of these from sample statistics
Population parameter: a numerically valued attribute of a model for a population. We rarely
expect to know the value of a parameter, but we do hope to estimate it from sampled data
Representative sample: a sample from which the statistics computed accurately reflect the
corresponding population parameters
LO 3.4 Simple Random Sampling (SRS)
• Sample-to-sample variability is to be expected
o If different sample from a population vary little from each other, then most likely the
underlying population harbors little variation
o If the samples show much sampling variability, the underlying population probably
varies alot
• We must strive to avoid bias
o Bias means that our sampling method distorts our view of the population
Simple random sample (SRS): a sample in which each set of n individuals in the population has
an equal chance of selection
• Standard against which we measure other sampling methods
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ECO220Y1 Full Course Notes
Verified Note
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Document Summary
Principle 1: examine a part of the whole: first step is to draw a sample. Population: the entire group of individuals or instances about whom we hope to learn. Sample: a subset of a population, examined in hopes of learning about the population. Randomization: a defence against bias in the sample selection process, in which each individual is given a fair, random chance of selection. Two things make randomization fair: nobody can guess the outcome before it happens, some underlying set of outcomes will be equally likely. Why not match the sample to the population: we can"t possibly think of all the relevant variables that might be important. Sampling variability: the natural tendency of randomly drawn samples to differ from one another. Principle 3: the sample size is what matters: the size of the sample determines what we can conclude from the data regardless of the size of the population, the size of the population does not matter at all.
