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Sensitivity and specificity of normality tests and consequences on reference interval accuracy at small sample size:

Kevin Le Boedec1

  • 1College of Veterinary Medicine, University of Illinois, Urbana, IL, USA.

Veterinary Clinical Pathology
|August 25, 2016
PubMed
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Normality tests struggle with small sample sizes, potentially leading to inaccurate reference intervals (RI) when using parametric methods. Nonparametric methods or adjusted significance levels are recommended for reliable RI construction.

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Area of Science:

  • Biostatistics
  • Clinical Laboratory Science

Background:

  • International guidelines recommend parametric methods for reference interval (RI) construction with small sample sizes assuming Gaussian distribution.
  • Normality tests may lack accuracy in identifying Gaussian distributions at small sample sizes.

Purpose of the Study:

  • Evaluate normality test performance for identifying Gaussian populations with small sample sizes.
  • Assess the impact of misclassifying non-Gaussian samples as Gaussian on RI accuracy when using parametric methods.

Main Methods:

  • Simulated Gaussian, lognormal, and asymmetric populations (n=10,000) were used.
  • Samples (n=60, n=30) were randomly selected 100 times.
  • Sensitivity and specificity of 4 normality tests were compared.
  • RI accuracy was assessed using 6 methods on samples falsely identified as Gaussian.

Main Results:

  • Shapiro-Wilk and D'Agostino-Pearson tests showed best performance but poor specificity at n=30.
  • Optimal significance levels for these tests at n=30 were found to be 0.19 and 0.18, respectively.
  • Applying parametric methods to lognormal samples misidentified as Gaussian caused clinically significant inaccuracies.

Conclusions:

  • Normality tests at small sample sizes can lead to incorrect use of parametric methods for RI construction.
  • Nonparametric methods or Box-Cox transformation are advised for all samples.
  • Adjusting normality test significance levels based on sample size can mitigate the risk of inaccurate RI.