Glossary

Section 8.8 Glossary

population mean 🔗: The true mean of a quantity in an entire population, as opposed to the sample mean, which is calculated from a subset.
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parameter 🔗: One of the values that specify a particular distribution in a set of distributions -- for example, the parameters of a normal distribution are the mean and standard deviation.
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estimator 🔗: A statistic calculated from a sample that is used to estimate a parameter of the population.
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consistent 🔗: An estimator is consistent if it converges to the actual value of a parameter as the sample size increases.
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unbiased 🔗: An estimator is unbiased if, for a particular sample size, the average of the sample estimates is the actual value of the parameter.
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mean squared error (MSE) 🔗: A measure of the accuracy of an estimator -- it’s the average squared difference between estimated and true parameter values, assuming the true value is known.
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robust 🔗: An estimator is robust if it remains accurate even when a dataset contains outliers or errors -- or does not perfectly follow a theoretical distribution.
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resampling 🔗: A way to approximate the sampling distribution of an estimate by simulating the sampling process.
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parametric resampling 🔗: A kind of resampling that estimates population parameters from sample data and then uses a theoretical distribution to simulate the sampling process.
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sampling distribution 🔗: The distribution of a statistic across possible samples from the same population.
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standard error 🔗: The standard deviation of a sampling distribution, which quantifies the variability of an estimate due to random sampling (but not measurement error or non-representative sampling).
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confidence interval 🔗: An interval that contains the most likely values in a sampling distribution.
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sampling bias 🔗: A flaw in the way a sample is collected that makes it unrepresentative of the population.
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measurement error 🔗: Inaccuracy in how data are observed, measured, or recorded.
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