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biased
vast digital repositories where data are recorded and stored electronically
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data warehouses
a sample that includes the entire population
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census
an entire collection of individuals
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population
when a variable names categories and answers questions about how cases fall into
those categories
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categorical/qualitative variable
when we slice the population into homogeneous groups, called strata, use simple
random sampling within each stratum, and combine the results at the end
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stratified random sampling
combining percentages across very different values or groups can give confusing
results because percentages are inappropriately combined
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simpson's paradox
a sample that estimates the corresponding parameters accurately
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representative
any intersection of a row and column of a contingency table that gives the count for a
combination of values of the two variables
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cell
when two or more separate data tables are linked together so that information can be
merged across them
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relational database
a large group of individuals is invited to respond, and all who do respond are
counted - results from these are almost always biased and wrong