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Updated: Jun 6, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

[Data transformation and normalization].

Toshinobu Nishida¹

¹Biomedical Laboratory Sciences, Institute of Health Biosciences, The University of Tokushima Graduate School, Tokushima 770-8509, Japan. nishida@medsci.tokushima-u.ac.jp

Rinsho Byori. the Japanese Journal of Clinical Pathology

|November 17, 2010

Summary

This summary is machine-generated.

Statistical analysis often requires normal distribution. Data transformation methods like square root or logarithmic are used when data is not normally distributed. Normal quantile plots offer a reliable way to assess data distribution.

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Last Updated: Jun 6, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Area of Science:

Statistical analysis
Data distribution

Context:

Parametric statistical methods require normally distributed data.
Assessing data normality is crucial before analysis.
Clinical reference intervals rely on accurate distribution assessment.

Purpose:

To review methods for assessing data normality.
To highlight the importance of data transformation when normality is not met.
To emphasize the reliability of normal quantile plots for distribution identification.

Summary:

Parametric statistical analysis necessitates normally distributed data.
Methods for checking normality include histograms, skewness/kurtosis, and the Kolmogorov-Smirnov test.
Power transformations (e.g., logarithmic) can normalize non-normally distributed data.
Normal quantile plots are suggested as a reliable method for identifying distribution types.

Impact:

Ensures appropriate statistical method selection.
Improves the accuracy of clinical reference intervals.
Enhances the reliability of scientific research findings.