Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

729
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
729
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.1K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.1K
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

1.6K
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
1.6K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

716
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
716
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

6.2K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
6.2K
Bonferroni Test01:10

Bonferroni Test

2.6K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
2.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Location of visual field defects and their impact on vision-related quality of life in glaucoma: A systematic review.

Optometry and vision science : official publication of the American Academy of Optometry·2026
Same author

Extravascular Motion Signal Detected by OCT Angiography Indicates Altered Vascular-Tissue Biomechanical Interactions in Glaucoma.

Investigative ophthalmology & visual science·2026
Same author

Glaucomatous Remodeling of the Lamina Cribrosa: Association With Visual Field Progression.

Investigative ophthalmology & visual science·2026
Same author

Microvascular Volume Loss Exceeds Nerve Fiber Layer but Not Neuroretinal Rim Tissue Loss During Progression of Nonhuman Primate Experimental Glaucoma.

Investigative ophthalmology & visual science·2026
Same author

Forecasting mean deviation in glaucoma patients using an irregular autoregressive time series method.

Scientific reports·2025
Same author

Automated Spectral-Domain Versus Swept-Source OCT Angiography in Relation to Glaucoma Severity.

Journal of glaucoma·2025

Related Experiment Video

Updated: May 1, 2026

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter
05:14

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter

Published on: September 16, 2025

820

Effect of a variability-adjusted algorithm on the efficiency of perimetric testing.

Stuart K Gardiner1

  • 1Discoveries in Sight Laboratories, Devers Eye Institute, Legacy Health, Portland, Oregon, United States.

Investigative Ophthalmology & Visual Science
|April 10, 2014
PubMed
Summary

A new variability-adjusted algorithm (VAA) improves visual field testing accuracy by adapting step size to patient variability. This method enhances efficiency, especially when initial sensitivity estimates are inaccurate, benefiting glaucoma patients.

Keywords:
computer simulationperimetrytesting algorithm

More Related Videos

Stereoacuity Improvement using Random-Dot Video Games
06:25

Stereoacuity Improvement using Random-Dot Video Games

Published on: January 14, 2020

15.0K
Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

7.7K

Related Experiment Videos

Last Updated: May 1, 2026

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter
05:14

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter

Published on: September 16, 2025

820
Stereoacuity Improvement using Random-Dot Video Games
06:25

Stereoacuity Improvement using Random-Dot Video Games

Published on: January 14, 2020

15.0K
Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

7.7K

Area of Science:

  • Ophthalmology
  • Visual Psychophysics
  • Medical Technology

Background:

  • Visual field testing variability increases with disease severity, hindering accurate sensitivity measurement.
  • Existing algorithms struggle to efficiently converge to true visual sensitivity due to this variability.
  • A novel approach is needed to improve the speed and accuracy of perimetric testing.

Purpose of the Study:

  • To introduce and evaluate a variability-adjusted algorithm (VAA) for visual field testing.
  • To assess if VAA can improve the convergence of testing algorithms by adjusting step size based on variability.
  • To compare VAA's performance against the Zippy Estimation by Sequential Testing (ZEST) algorithm.

Main Methods:

  • Simulated a Bayesian thresholding procedure using a modified ZEST algorithm on a transformed scale where standard deviation is constant.
  • Converted results back to decibels and compared root-mean-squared (RMS) error against standard ZEST.
  • Repeated simulations with a lower sensitivity limit of 15 dB to evaluate performance in reliable ranges.

Main Results:

  • When initial and true sensitivities matched (35 dB), VAA and ZEST showed similar RMS errors (1.39–1.60 dB).
  • When initial sensitivity was overestimated (35 dB vs. 20 dB true), VAA significantly reduced RMS error from 7.43 dB to 3.66 dB.
  • Limiting sensitivities to ≥15 dB generally improved efficiency, except when true sensitivity was near the 15 dB threshold.

Conclusions:

  • The variability-adjusted algorithm (VAA) effectively reduces perimetric variability and improves accuracy without increasing test time, particularly when initial sensitivity estimates are high.
  • Setting a lower limit for sensitivity testing (e.g., 15 dB) enhances algorithm efficiency, though care must be taken if true sensitivity is near this limit.
  • VAA represents a promising new class of visual field testing algorithms that could benefit patients, especially those with early or small scotomas.