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Related Concept Videos

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P-value is one of the most crucial concepts in statistics.
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Related Experiment Video

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P-values in genomics: apparent precision masks high uncertainty.

L C Lazzeroni1, Y Lu2, I Belitskaya-Lévy3

  • 1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, CA, USA.

Molecular Psychiatry
|January 15, 2014
PubMed
Summary

P-values are highly variable, making replication studies uncertain. This research introduces prediction intervals to quantify P-value variability, improving the interpretation of statistical findings in genomics and other fields.

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Area of Science:

  • Statistics
  • Genomics
  • Biostatistics

Background:

  • P-values are commonly used in large-scale genomic studies to assess statistical significance and guide subsequent research.
  • However, the inherent variability of P-values can lead to overinterpretation and a high incidence of non-replication in scientific findings.

Purpose of the Study:

  • To examine P-value variability and assess the certainty they provide in statistical findings.
  • To develop prediction intervals for P-values in replication studies based on initial study P-values and sample size ratios.

Main Methods:

  • Development of prediction intervals for P-values in replication studies.
  • The intervals are dependent on the initial P-value and the ratio of sample sizes between initial and replication studies.
  • Application of the method to Alzheimer's disease data and Psychiatric Genomics Consortium findings.

Main Results:

  • P-values exhibit significant variability, but this variability can be explicitly predicted.
  • The relative sample size between initial and replication studies is a key predictor of future P-values.
  • A calculator is provided to implement these prediction intervals.

Conclusions:

  • Overinterpretation of highly significant, yet variable, P-values contributes to non-replication.
  • Formal prediction intervals offer realistic interpretations and comparisons of P-values, accounting for effect size and sample size variations.
  • This approach enhances the reliability of statistical findings, particularly in large-scale research.