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NMR Spectrometers: Resolution and Error Correction01:14

NMR Spectrometers: Resolution and Error Correction

When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
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Correction for multiple testing: is there a resolution?

David L Streiner1, Geoffrey R Norman2

  • 1Department of Psychiatry and Behavioural Neurosciences, Hamilton, ON, Canada; Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.

Chest
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PubMed
Summary
This summary is machine-generated.

This study addresses the problem of multiplicity in statistical testing, where numerous tests increase the chance of false positives. It provides guidance on when to use and not use multiplicity adjustment procedures like the Bonferroni correction.

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Area of Science:

  • Statistical methodology
  • Biostatistics
  • Clinical trial design

Background:

  • Multiple statistical tests are common in research, used for baseline comparisons, assessing relationships, and evaluating multiple endpoints.
  • Increasing the number of tests elevates the risk of obtaining statistically significant results purely by chance, known as the problem of multiplicity.
  • Existing methods like the Bonferroni correction aim to address multiplicity, but their application remains a subject of debate.

Purpose of the Study:

  • To provide clear recommendations on the appropriate use of statistical procedures for handling multiplicity.
  • To clarify when multiplicity adjustment methods should and should not be applied in research studies.
  • To reduce the likelihood of false-positive findings in studies with numerous statistical tests.

Main Methods:

  • Review of statistical literature and common practices in research studies.
  • Analysis of the impact of multiplicity on statistical significance.
  • Development of evidence-based recommendations for applying multiplicity correction procedures.

Main Results:

  • The probability of false positives increases significantly with the number of statistical tests performed.
  • Controversy exists regarding the consistent application of multiplicity adjustment methods across different research contexts.
  • Guidelines are proposed to aid researchers in deciding when to employ multiplicity correction techniques.

Conclusions:

  • Appropriate use of multiplicity adjustment procedures is crucial for maintaining the integrity of research findings.
  • Recommendations are offered to guide researchers in navigating the complexities of multiplicity in statistical analysis.
  • Adherence to these guidelines can enhance the reliability and validity of study results by mitigating the risk of chance findings.