Parametric Statistical Inference for Comparing Means and Variances.

Investigative Ophthalmology & Visual Science
Johannes LedolterRandy H Kardon

Abstract

The purpose of this tutorial is to provide visual scientists with various approaches for comparing two or more groups of data using parametric statistical tests, which require that the distribution of data within each group is normal (Gaussian). Non-parametric tests are used for inference when the sample data are not normally distributed or the sample is too small to assess its true distribution. Methods are reviewed using retinal thickness, as measured by optical coherence tomography (OCT), as an example for comparing two or more group means. The following parametric statistical approaches are presented for different situations: two-sample t-test, Analysis of Variance (ANOVA), paired t-test, and the analysis of repeated measures data using a linear mixed-effects model approach. Analyzing differences between means using various approaches is demonstrated, and follow-up procedures to analyze pairwise differences between means when there are more than two comparison groups are discussed. The assumption of equal variance between groups and methods to test for equal variances are examined. Examples of repeated measures analysis for right and left eyes on subjects, across spatial segments within the same eye (e.g. quadrants of each ...Continue Reading

References

Jan 1, 2008·Statistics Surveys·Mingyan Huang, Daowen Zhang
Jun 13, 2020·Investigative Ophthalmology & Visual Science·Johannes LedolterRandy H Kardon

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Citations

Jul 24, 2021·Annual Review of Vision Science·Maureen G Maguire

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Software Mentioned

Minitab
GraphPad Prism8
Excel
SAS
Prism
GraphPad
GraphPad Prism
R

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