Cross-validation
In statistics cross-validation is the practice of partitioning a sample of data into subsamples such that analysis is initially performed on a single subsample, while further subsamples are retained "blind" in order for subsequent use in confirming and validating the initial analysis.
Cross-validation is important in guarding against testing hypotheses suggested by the data, especially where further samples are hazardous, costly or impossible (uncomfortable science) to collect.
Referenced By
List of statistical topics | ProbabilityApplications | Probability Applications | Testing effects suggested by the data | Testing hypotheses suggested by the data
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