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

Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
Introduction to z Scores01:06

Introduction to z Scores

A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores help...
Introduction to z Scores01:05

Introduction to z Scores

A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores help...
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
z Scores and Unusual Values01:07

z Scores and Unusual Values

The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
 This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data value...
z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of zero.

You might also read

Related Articles

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

Sort by
Same author

Thermal gradient effects in optical testing of large astronomical mirrors: comments.

Applied optics·2010
Same author

Zernike test. 2: Experimental aspects.

Applied optics·2010
Same author

Dynamic Hartmann test.

Applied optics·2010
Same author

Wavefront error simulator for evaluating optical testing instrumentation.

Applied optics·2010
Same journal

Multifunctional reconfigurable terahertz metasurface based on vanadium dioxide phase transition: achieving broadband absorption and efficient polarization conversion.

Applied optics·2026
Same journal

High-Q-factor electromagnetically induced transparency utilizing quasi-bound states in the continuum in an all-dielectric terahertz metasurface.

Applied optics·2026
Same journal

Automated stitching interferometry for high-precision metrology of X-ray mirrors.

Applied optics·2026
Same journal

Experimental demonstration of an approach to designing a metal-dielectric DBR resonant cavity structure.

Applied optics·2026
Same journal

High-precision wavefront reconstruction from a single-shot interferogram using a physics-driven hybrid feature calibration network.

Applied optics·2026
Same journal

Ultra-high-Q Fano resonance based on coupled topological corner states in Kagome photonic crystals.

Applied optics·2026
See all related articles

Related Experiment Video

Updated: Jun 16, 2026

Advancing Dyslexia Assessment in Children Through Computerized Testing
09:00

Advancing Dyslexia Assessment in Children Through Computerized Testing

Published on: August 16, 2024

Zernike test. 1: Analytical aspects.

L J Golden

    Applied Optics
    |February 20, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study optimizes Zernike test parameters for improved sensitivity and linear range in aberration analysis. The findings enhance the Zernike test

    More Related Videos

    Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
    16:23

    Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

    Published on: February 26, 2014

    Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
    05:58

    Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

    Published on: March 22, 2022

    Related Experiment Videos

    Last Updated: Jun 16, 2026

    Advancing Dyslexia Assessment in Children Through Computerized Testing
    09:00

    Advancing Dyslexia Assessment in Children Through Computerized Testing

    Published on: August 16, 2024

    Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
    16:23

    Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

    Published on: February 26, 2014

    Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
    05:58

    Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

    Published on: March 22, 2022

    Area of Science:

    • Optical testing and metrology
    • Aberration analysis
    • Interferometry

    Background:

    • The Zernike test is a standard method for optical testing.
    • Interferometric interpretation is crucial for understanding test performance.
    • Optimization of test parameters is needed for enhanced sensitivity and linearity.

    Purpose of the Study:

    • To optimize Zernike disk parameters (radius, phase, transmittance) for low-order aberrations.
    • To develop a method for selecting disk parameters to maximize test sensitivity and linear range.
    • To analyze the signal-to-noise ratio for predicting test sensitivity.

    Main Methods:

    • Interferometric analysis of the Zernike test.
    • Optimization of Zernike disk parameters.
    • Signal-to-noise ratio analysis.

    Main Results:

    • A method for optimizing Zernike disk parameters is presented.
    • Optimized parameters enhance test sensitivity and linear range.
    • Signal-to-noise analysis predicts sensitivity better than lambda/100.

    Conclusions:

    • The Zernike test can be optimized for improved performance in aberration analysis.
    • Parameter optimization leads to enhanced sensitivity and linear range.
    • The proposed method provides a systematic approach to Zernike test optimization.