Generalization
Though there is often an instinct to use data to make large generalized claims, the applicability of inferences and claims that are made are constrained by the sample, population, and context of the data.
Generalization
Though there is often an instinct to use data to make large generalized claims, the applicability of inferences and claims that are made are constrained by the sample, population, and context of the data.
Application fitness
Regularly identify generalization issues, with frequent comparisons between significant real-world examples and a current analysis.
Sample size
When full information is hidden or inaccessible, recognize the logical relationship between a sufficient number of chances and a sufficiently large sample to reasonably represent something.
Sample bias
When information is completely hidden or unavailable, be aware of possible underlying issues in the sample and apply strategies to identify and address them.
Subset effects
Recognize that important information may be hidden or may even change a major conclusion when data is filtered into categories and/or groups.
Meta-analysis & facts
Recognize the relationship between many trials, uncertainty, and whether a claim is a “fact.”
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