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Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
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Variation01:19

Variation

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
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What is Variation?01:14

What is Variation?

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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
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One-Way ANOVA: Equal Sample Sizes01:15

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Variance01:15

Variance

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The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
The standard deviation measures the spread in the same units as the data....
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A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
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Variance components affecting the repeatability of the alternating cover test.

Marius M Paulus1, Andreas Straube1, Thomas Eggert1

  • 1University Hospital, LMU Munich, Germany.

Journal of Eye Movement Research
|April 8, 2021
PubMed
Summary

Variability in heterophoria measurements is primarily due to changes in the condition itself, not just measurement or stimulus errors. This "heterophoria noise" is largely influenced by periods of binocular viewing between tests.

Keywords:
Heterophoriacover testeye movementeye trackinggazereliabilityvergence

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Area of Science:

  • Ophthalmology
  • Vision Science
  • Optometry

Background:

  • Manual prism cover tests for heterophoria have significant measurement variability (SD 0.5-0.8 deg).
  • This variability, or

Purpose of the Study:

  • To differentiate sources of error in heterophoria measurement: examiner (measurement noise), heterophoria size (heterophoria noise), and sensory vergence cues (stimulus noise).
  • To develop and evaluate an automated cover test to minimize stimulus noise and quantify heterophoria noise.

Main Methods:

  • Developed an automated alternating cover test using VOG and shutter glasses, achieving a measurement noise of SD=0.06 deg.
  • Conducted within-subject repeated measures, with blocks of 19 measures taken over short intervals and repeated across days or within 45 minutes, separated by binocular viewing.

Main Results:

  • The standard deviation of heterophoria across blocks (SD=0.33 deg) was 6 times greater than within-block variability.
  • Approximately 42% of the inter-block variance in manual prism cover tests was attributed to heterophoria variability, not measurement or stimulus noise.
  • Heterophoria noise was primarily induced during intervening binocular viewing periods.

Conclusions:

  • Heterophoria measurements are significantly influenced by inherent fluctuations in the patient's condition (heterophoria noise).
  • The automated cover test effectively reduces measurement and stimulus noise, isolating heterophoria noise.
  • Binocular viewing periods between tests are a key factor in inducing heterophoria variability.