Matches in DBpedia 2016-04 for { ?s ?p "In statistics, per-comparison error rate (PCER) is the probability of a result in the absence of any formal multiple hypothesis testing correction. Typically, when considering a result under many hypotheses, some tests will give false positives; many statisticians make use of Bonferroni correction, false discovery rate, and other methods to determine the odds of a negative result appearing to be positive."@en }
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- Per-comparison_error_rate abstract "In statistics, per-comparison error rate (PCER) is the probability of a result in the absence of any formal multiple hypothesis testing correction. Typically, when considering a result under many hypotheses, some tests will give false positives; many statisticians make use of Bonferroni correction, false discovery rate, and other methods to determine the odds of a negative result appearing to be positive.".
- Q7166620 abstract "In statistics, per-comparison error rate (PCER) is the probability of a result in the absence of any formal multiple hypothesis testing correction. Typically, when considering a result under many hypotheses, some tests will give false positives; many statisticians make use of Bonferroni correction, false discovery rate, and other methods to determine the odds of a negative result appearing to be positive.".
- Per-comparison_error_rate comment "In statistics, per-comparison error rate (PCER) is the probability of a result in the absence of any formal multiple hypothesis testing correction. Typically, when considering a result under many hypotheses, some tests will give false positives; many statisticians make use of Bonferroni correction, false discovery rate, and other methods to determine the odds of a negative result appearing to be positive.".
- Q7166620 comment "In statistics, per-comparison error rate (PCER) is the probability of a result in the absence of any formal multiple hypothesis testing correction. Typically, when considering a result under many hypotheses, some tests will give false positives; many statisticians make use of Bonferroni correction, false discovery rate, and other methods to determine the odds of a negative result appearing to be positive.".